WHO: LifeExpectancy regression: WHO2
bdanalytics
Date: (Thu) Jun 04, 2015
Introduction:
Data: Source: Training: https://courses.edx.org/asset-v1:MITx+15.071x_2a+2T2015+type@asset+block/WHO.csv
New:
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glb_txt_vars
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Convert user-specified mutate code to config specs
- Add n-grams for glb_txt_vars
- fit.models chunk:
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- move model_type parameter to myfit_mdl before indep_vars_vctr (keep all model_* together)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- Skip trControl.method=“cv” for dummy classifier ?
- Add custom model to caret for a dummy (baseline) classifier (binomial & multinomial) that generates proba/outcomes which mimics the freq distribution of glb_rsp_var values; Right now glb_dmy_glm_mdl always generates most frequent outcome in training data
glm_dmy_mdl should use the same method as glm_sel_mdl until custom dummy classifer is implemented
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Replicate myfit_mdl_classification features in myfit_mdl_regression
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list=ls())
set.seed(12345)
options(stringsAsFactors=FALSE)
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mydsutils.R")## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
registerDoMC(4) # max(length(glb_txt_vars), glb_n_cv_folds) + 1
#packageVersion("snow")
#require(sos); findFn("pinv", maxPages=2, sortby="MaxScore")
# Analysis control global variables
glb_trnng_url <- "https://courses.edx.org/asset-v1:MITx+15.071x_2a+2T2015+type@asset+block/WHO.csv"
glb_newdt_url <- "<newdt_url>"
glb_out_pfx <- "WHO2_"
glb_save_envir <- FALSE # or TRUE
glb_is_separate_newent_dataset <- FALSE # or TRUE
glb_split_entity_newent_datasets <- TRUE # or FALSE
glb_split_newdata_method <- "sample" # "condition" or "sample" or "copy"
glb_split_newdata_condition <- "<col_name> <condition_operator> <value>" # or NULL
glb_split_newdata_size_ratio <- 0.3 # > 0 & < 1
glb_split_sample.seed <- 123 # or any integer
glb_drop_vars <- c(NULL) # or c("<col_name>")
glb_max_fitent_obs <- NULL # or any integer
glb_is_regression <- TRUE; glb_is_classification <- FALSE; glb_is_binomial <- TRUE
glb_rsp_var_raw <- "LifeExpectancy"
# for classification, the response variable has to be a factor
glb_rsp_var <- glb_rsp_var_raw # or "LifeExpectancy.fctr"
# if the response factor is based on numbers e.g (0/1 vs. "A"/"B"),
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- NULL # or function(raw) {
#relevel(factor(ifelse(raw == 1, "Y", "N")), as.factor(c("Y", "N")), ref="N")
#as.factor(paste0("B", raw))
#as.factor(raw)
#}
#glb_map_rsp_raw_to_var(c(1, 1, 0, 0, 0))
glb_map_rsp_var_to_raw <- NULL # or function(var) {
#as.numeric(var) - 1
#as.numeric(var)
#levels(var)[as.numeric(var)]
#c(" <=50K", " >50K")[as.numeric(var)]
#}
#glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(c(1, 1, 0, 0, 0)))
if ((glb_rsp_var != glb_rsp_var_raw) & is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
glb_rsp_var_out <- paste0(glb_rsp_var, ".predict.") # model_id is appended later
# List info gathered for various columns
# <col_name>: <description>; <notes>
glb_id_vars <- c("Country")
glb_category_vars <- NULL # or c("<var1>", "<var2>")
glb_date_vars <- NULL # or c("<date_var>")
glb_txt_vars <- NULL # or c("<txt_var1>", "<txt_var2>")
#Sys.setlocale("LC_ALL", "C") # For english
glb_append_stop_words <- list()
# Remember to use unstemmed words
#orderBy(~ -cor.y.abs, subset(glb_feats_df, grepl("[HSA]\\.T\\.", id) & !is.na(cor.high.X)))
#dsp_obs(Headline.contains="polit")
#subset(glb_allobs_df, H.T.compani > 0)[, c("UniqueID", "Headline", "H.T.compani")]
# glb_append_stop_words[["<txt_var1>"]] <- c(NULL
# # ,"<word1>" # <reason1>
# )
#subset(glb_allobs_df, S.T.newyorktim > 0)[, c("UniqueID", "Snippet", "S.T.newyorktim")]
#glb_txt_lst[["Snippet"]][which(glb_allobs_df$UniqueID %in% c(8394, 8317, 8339, 8350, 8307))]
glb_important_terms <- list()
# Remember to use stemmed terms
glb_sprs_thresholds <- NULL # or c(0.988, 0.970, 0.970) # Generates 29, 22, 22 terms
# Properties:
# numrows(glb_feats_df) << numrows(glb_fitobs_df)
# Select terms that appear in at least 0.2 * O(FP/FN(glb_OOBobs_df))
# numrows(glb_OOBobs_df) = 1.1 * numrows(glb_newobs_df)
names(glb_sprs_thresholds) <- glb_txt_vars
glb_log_vars <- NULL # or c("<numeric_var1>", "<numeric_var2>")
# List transformed vars
glb_exclude_vars_as_features <- c(NULL) # or c("<var_name>")
if (glb_rsp_var_raw != glb_rsp_var)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_rsp_var_raw)
if (!is.null(glb_txt_vars))
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_txt_vars)
# List feats that shd be excluded due to known causation by prediction variable
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(NULL)) # or c("<col_name>")
glb_interaction_only_features <- NULL # or ???
glb_force_0_to_NA_vars <- NULL # or c("<numeric_var1>", "<numeric_var2>")
glb_impute_na_data <- TRUE # or FALSE
glb_mice_complete.seed <- 144 # or any integer
glb_cluster <- FALSE # or TRUE
glb_models_lst <- list(); glb_models_df <- data.frame()
# Regression
if (glb_is_regression)
glb_models_method_vctr <- c("lm", "glm", "rpart", "rf") else
# Classification
if (glb_is_binomial)
glb_models_method_vctr <- c("glm", "bayesglm", "rpart", "rf") else
glb_models_method_vctr <- c("rpart", "rf")
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<col_name>")
glb_model_metric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glb_model_metric <- NULL # or "<metric_name>"
glb_model_metric_maximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glb_model_metric_smmry <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glb_model_metric_terms)
# metric <- sum(confusion_mtrx * glb_model_metric_terms) / nrow(data)
# names(metric) <- glb_model_metric
# return(metric)
# }
glb_tune_models_df <-
rbind(
#data.frame(parameter="cp", min=0.00005, max=0.00005, by=0.000005),
#seq(from=0.01, to=0.01, by=0.01)
#data.frame(parameter="mtry", min=080, max=100, by=10),
#data.frame(parameter="mtry", min=08, max=10, by=1),
data.frame(parameter="dummy", min=2, max=4, by=1)
)
# or NULL
glb_n_cv_folds <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glb_model_evl_criteria <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glb_model_evl_criteria <-
c("max.Accuracy.OOB", "max.auc.OOB", "max.Kappa.OOB", "min.aic.fit") else
glb_model_evl_criteria <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
glb_sel_mdl_id <- NULL # or "<model_id_prefix>.<model_method>"
glb_fin_mdl_id <- glb_sel_mdl_id # or "Final"
# Depict process
glb_analytics_pn <- petrinet(name="glb_analytics_pn",
trans_df=data.frame(id=1:6,
name=c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df=data.frame(
begin=c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end =c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL, "import.data")## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 7.716 NA NAStep 1.0: import data
chunk option: eval=
glb_trnobs_df <- myimport_data(url=glb_trnng_url, comment="glb_trnobs_df",
force_header=TRUE)## [1] "Reading file ./data/WHO.csv..."
## [1] "dimensions of data in ./data/WHO.csv: 194 rows x 13 cols"
## Country Region Population Under15 Over60
## 1 Afghanistan Eastern Mediterranean 29825 47.42 3.82
## 2 Albania Europe 3162 21.33 14.93
## 3 Algeria Africa 38482 27.42 7.17
## 4 Andorra Europe 78 15.20 22.86
## 5 Angola Africa 20821 47.58 3.84
## 6 Antigua and Barbuda Americas 89 25.96 12.35
## FertilityRate LifeExpectancy ChildMortality CellularSubscribers
## 1 5.40 60 98.5 54.26
## 2 1.75 74 16.7 96.39
## 3 2.83 73 20.0 98.99
## 4 NA 82 3.2 75.49
## 5 6.10 51 163.5 48.38
## 6 2.12 75 9.9 196.41
## LiteracyRate GNI PrimarySchoolEnrollmentMale
## 1 NA 1140 NA
## 2 NA 8820 NA
## 3 NA 8310 98.2
## 4 NA NA 78.4
## 5 70.1 5230 93.1
## 6 99.0 17900 91.1
## PrimarySchoolEnrollmentFemale
## 1 NA
## 2 NA
## 3 96.4
## 4 79.4
## 5 78.2
## 6 84.5
## Country Region Population Under15 Over60
## 7 Argentina Americas 41087 24.42 14.97
## 29 Cambodia Western Pacific 14865 31.23 7.67
## 99 Lithuania Europe 3028 15.13 20.57
## 140 Republic of Moldova Europe 3514 16.52 16.72
## 141 Romania Europe 21755 15.05 20.66
## 191 Viet Nam Western Pacific 90796 22.87 9.32
## FertilityRate LifeExpectancy ChildMortality CellularSubscribers
## 7 2.20 76 14.2 134.92
## 29 2.93 65 39.7 96.17
## 99 1.49 74 5.4 151.30
## 140 1.47 71 17.6 104.80
## 141 1.39 74 12.2 109.16
## 191 1.79 75 23.0 143.39
## LiteracyRate GNI PrimarySchoolEnrollmentMale
## 7 97.8 17130 NA
## 29 NA 2230 96.4
## 99 99.7 19640 95.6
## 140 98.5 3640 90.1
## 141 97.7 15120 87.9
## 191 93.2 3250 NA
## PrimarySchoolEnrollmentFemale
## 7 NA
## 29 95.4
## 99 95.8
## 140 90.1
## 141 87.3
## 191 NA
## Country Region Population
## 189 Vanuatu Western Pacific 247
## 190 Venezuela (Bolivarian Republic of) Americas 29955
## 191 Viet Nam Western Pacific 90796
## 192 Yemen Eastern Mediterranean 23852
## 193 Zambia Africa 14075
## 194 Zimbabwe Africa 13724
## Under15 Over60 FertilityRate LifeExpectancy ChildMortality
## 189 37.37 6.02 3.46 72 17.9
## 190 28.84 9.17 2.44 75 15.3
## 191 22.87 9.32 1.79 75 23.0
## 192 40.72 4.54 4.35 64 60.0
## 193 46.73 3.95 5.77 55 88.5
## 194 40.24 5.68 3.64 54 89.8
## CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale
## 189 55.76 82.6 4330 NA
## 190 97.78 NA 12430 94.7
## 191 143.39 93.2 3250 NA
## 192 47.05 63.9 2170 85.5
## 193 60.59 71.2 1490 91.4
## 194 72.13 92.2 NA NA
## PrimarySchoolEnrollmentFemale
## 189 NA
## 190 95.1
## 191 NA
## 192 70.5
## 193 93.9
## 194 NA
## 'data.frame': 194 obs. of 13 variables:
## $ Country : chr "Afghanistan" "Albania" "Algeria" "Andorra" ...
## $ Region : chr "Eastern Mediterranean" "Europe" "Africa" "Europe" ...
## $ Population : int 29825 3162 38482 78 20821 89 41087 2969 23050 8464 ...
## $ Under15 : num 47.4 21.3 27.4 15.2 47.6 ...
## $ Over60 : num 3.82 14.93 7.17 22.86 3.84 ...
## $ FertilityRate : num 5.4 1.75 2.83 NA 6.1 2.12 2.2 1.74 1.89 1.44 ...
## $ LifeExpectancy : int 60 74 73 82 51 75 76 71 82 81 ...
## $ ChildMortality : num 98.5 16.7 20 3.2 163.5 ...
## $ CellularSubscribers : num 54.3 96.4 99 75.5 48.4 ...
## $ LiteracyRate : num NA NA NA NA 70.1 99 97.8 99.6 NA NA ...
## $ GNI : num 1140 8820 8310 NA 5230 ...
## $ PrimarySchoolEnrollmentMale : num NA NA 98.2 78.4 93.1 91.1 NA NA 96.9 NA ...
## $ PrimarySchoolEnrollmentFemale: num NA NA 96.4 79.4 78.2 84.5 NA NA 97.5 NA ...
## - attr(*, "comment")= chr "glb_trnobs_df"
## NULLif (glb_is_separate_newent_dataset) {
glb_newobs_df <- myimport_data(url=glb_newdt_url, comment="glb_newobs_df",
force_header=TRUE)
# To make plots / stats / checks easier in chunk:inspectORexplore.data
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df);
comment(glb_allobs_df) <- "glb_allobs_df"
} else {
glb_allobs_df <- glb_trnobs_df; comment(glb_allobs_df) <- "glb_allobs_df"
if (!glb_split_entity_newent_datasets) {
stop("Not implemented yet")
glb_newobs_df <- glb_trnobs_df[sample(1:nrow(glb_trnobs_df),
max(2, nrow(glb_trnobs_df) / 1000)),]
} else if (glb_split_newdata_method == "condition") {
glb_newobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=glb_split_newdata_condition)))
glb_trnobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=paste0("!(",
glb_split_newdata_condition,
")"))))
} else if (glb_split_newdata_method == "sample") {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=(1-glb_split_newdata_size_ratio))
glb_newobs_df <- glb_trnobs_df[!split, ]
glb_trnobs_df <- glb_trnobs_df[split ,]
} else if (glb_split_newdata_method == "copy") {
glb_trnobs_df <- glb_allobs_df
comment(glb_trnobs_df) <- "glb_trnobs_df"
glb_newobs_df <- glb_allobs_df
comment(glb_newobs_df) <- "glb_newobs_df"
} else stop("glb_split_newdata_method should be %in% c('condition', 'sample', 'copy')")
comment(glb_newobs_df) <- "glb_newobs_df"
myprint_df(glb_newobs_df)
str(glb_newobs_df)
if (glb_split_entity_newent_datasets) {
myprint_df(glb_trnobs_df)
str(glb_trnobs_df)
}
} ## Loading required package: caTools## Country Region Population Under15 Over60 FertilityRate
## 3 Algeria Africa 38482 27.42 7.17 2.83
## 4 Andorra Europe 78 15.20 22.86 NA
## 5 Angola Africa 20821 47.58 3.84 6.10
## 7 Argentina Americas 41087 24.42 14.97 2.20
## 8 Armenia Europe 2969 20.34 14.06 1.74
## 9 Australia Western Pacific 23050 18.95 19.46 1.89
## LifeExpectancy ChildMortality CellularSubscribers LiteracyRate GNI
## 3 73 20.0 98.99 NA 8310
## 4 82 3.2 75.49 NA NA
## 5 51 163.5 48.38 70.1 5230
## 7 76 14.2 134.92 97.8 17130
## 8 71 16.4 103.57 99.6 6100
## 9 82 4.9 108.34 NA 38110
## PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 3 98.2 96.4
## 4 78.4 79.4
## 5 93.1 78.2
## 7 NA NA
## 8 NA NA
## 9 96.9 97.5
## Country Region Population Under15
## 16 Belarus Europe 9405 15.10
## 68 Greece Europe 11125 14.60
## 81 Iraq Eastern Mediterranean 32778 40.51
## 86 Japan Western Pacific 127000 13.12
## 126 Niue Western Pacific 1 30.61
## 185 United Republic of Tanzania Africa 47783 44.85
## Over60 FertilityRate LifeExpectancy ChildMortality CellularSubscribers
## 16 19.31 1.47 71 5.2 111.88
## 68 25.41 1.51 81 4.8 106.48
## 81 4.95 4.15 69 34.4 78.12
## 86 31.92 1.39 83 3.0 104.95
## 126 9.07 NA 72 25.1 NA
## 185 4.89 5.36 59 54.0 55.53
## LiteracyRate GNI PrimarySchoolEnrollmentMale
## 16 NA 14460 NA
## 68 97.2 25100 98.8
## 81 78.2 3750 NA
## 86 NA 35330 NA
## 126 NA NA NA
## 185 73.2 1500 NA
## PrimarySchoolEnrollmentFemale
## 16 NA
## 68 99.3
## 81 NA
## 86 NA
## 126 NA
## 185 NA
## Country Region Population Under15
## 171 Thailand South-East Asia 66785 18.47
## 177 Tunisia Eastern Mediterranean 10875 23.22
## 185 United Republic of Tanzania Africa 47783 44.85
## 191 Viet Nam Western Pacific 90796 22.87
## 192 Yemen Eastern Mediterranean 23852 40.72
## 193 Zambia Africa 14075 46.73
## Over60 FertilityRate LifeExpectancy ChildMortality CellularSubscribers
## 171 13.96 1.43 74 13.2 111.63
## 177 10.49 2.04 76 16.1 116.93
## 185 4.89 5.36 59 54.0 55.53
## 191 9.32 1.79 75 23.0 143.39
## 192 4.54 4.35 64 60.0 47.05
## 193 3.95 5.77 55 88.5 60.59
## LiteracyRate GNI PrimarySchoolEnrollmentMale
## 171 NA 8360 NA
## 177 NA 9030 NA
## 185 73.2 1500 NA
## 191 93.2 3250 NA
## 192 63.9 2170 85.5
## 193 71.2 1490 91.4
## PrimarySchoolEnrollmentFemale
## 171 NA
## 177 NA
## 185 NA
## 191 NA
## 192 70.5
## 193 93.9
## 'data.frame': 55 obs. of 13 variables:
## $ Country : chr "Algeria" "Andorra" "Angola" "Argentina" ...
## $ Region : chr "Africa" "Europe" "Africa" "Americas" ...
## $ Population : int 38482 78 20821 41087 2969 23050 1318 9405 2004 199000 ...
## $ Under15 : num 27.4 15.2 47.6 24.4 20.3 ...
## $ Over60 : num 7.17 22.86 3.84 14.97 14.06 ...
## $ FertilityRate : num 2.83 NA 6.1 2.2 1.74 1.89 2.12 1.47 2.71 1.82 ...
## $ LifeExpectancy : int 73 82 51 76 71 82 79 71 66 74 ...
## $ ChildMortality : num 20 3.2 163.5 14.2 16.4 ...
## $ CellularSubscribers : num 99 75.5 48.4 134.9 103.6 ...
## $ LiteracyRate : num NA NA 70.1 97.8 99.6 NA 91.9 NA 84.5 NA ...
## $ GNI : num 8310 NA 5230 17130 6100 ...
## $ PrimarySchoolEnrollmentMale : num 98.2 78.4 93.1 NA NA 96.9 NA NA NA NA ...
## $ PrimarySchoolEnrollmentFemale: num 96.4 79.4 78.2 NA NA 97.5 NA NA NA NA ...
## - attr(*, "comment")= chr "glb_newobs_df"
## Country Region Population Under15 Over60
## 1 Afghanistan Eastern Mediterranean 29825 47.42 3.82
## 2 Albania Europe 3162 21.33 14.93
## 6 Antigua and Barbuda Americas 89 25.96 12.35
## 10 Austria Europe 8464 14.51 23.52
## 11 Azerbaijan Europe 9309 22.25 8.24
## 12 Bahamas Americas 372 21.62 11.24
## FertilityRate LifeExpectancy ChildMortality CellularSubscribers
## 1 5.40 60 98.5 54.26
## 2 1.75 74 16.7 96.39
## 6 2.12 75 9.9 196.41
## 10 1.44 81 4.0 154.78
## 11 1.96 71 35.2 108.75
## 12 1.90 75 16.9 86.06
## LiteracyRate GNI PrimarySchoolEnrollmentMale
## 1 NA 1140 NA
## 2 NA 8820 NA
## 6 99 17900 91.1
## 10 NA 42050 NA
## 11 NA 8960 85.3
## 12 NA NA NA
## PrimarySchoolEnrollmentFemale
## 1 NA
## 2 NA
## 6 84.5
## 10 NA
## 11 84.1
## 12 NA
## Country Region Population
## 48 Democratic Republic of the Congo Africa 65705
## 80 Iran (Islamic Republic of) Eastern Mediterranean 76424
## 103 Malaysia Western Pacific 29240
## 116 Mozambique Africa 25203
## 165 Suriname Americas 535
## 184 United Kingdom Europe 62783
## Under15 Over60 FertilityRate LifeExpectancy ChildMortality
## 48 45.11 4.51 6.15 49 145.7
## 80 23.68 7.82 1.91 73 17.6
## 103 26.65 8.21 1.99 74 8.5
## 116 45.38 5.01 5.34 53 89.7
## 165 27.83 9.55 2.32 72 20.8
## 184 17.54 23.06 1.90 80 4.8
## CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale
## 48 23.09 66.8 340 NA
## 80 74.93 NA NA NA
## 103 127.04 93.1 15650 NA
## 116 32.83 56.1 970 94.6
## 165 178.88 94.7 NA NA
## 184 130.75 NA 36010 99.8
## PrimarySchoolEnrollmentFemale
## 48 NA
## 80 NA
## 103 NA
## 116 89.4
## 165 NA
## 184 99.6
## Country Region Population Under15
## 186 United States of America Americas 318000 19.63
## 187 Uruguay Americas 3395 22.05
## 188 Uzbekistan Europe 28541 28.90
## 189 Vanuatu Western Pacific 247 37.37
## 190 Venezuela (Bolivarian Republic of) Americas 29955 28.84
## 194 Zimbabwe Africa 13724 40.24
## Over60 FertilityRate LifeExpectancy ChildMortality CellularSubscribers
## 186 19.31 2.00 79 7.1 92.72
## 187 18.59 2.07 77 7.2 140.75
## 188 6.38 2.38 68 39.6 91.65
## 189 6.02 3.46 72 17.9 55.76
## 190 9.17 2.44 75 15.3 97.78
## 194 5.68 3.64 54 89.8 72.13
## LiteracyRate GNI PrimarySchoolEnrollmentMale
## 186 NA 48820 95.4
## 187 98.1 14640 NA
## 188 99.4 3420 93.3
## 189 82.6 4330 NA
## 190 NA 12430 94.7
## 194 92.2 NA NA
## PrimarySchoolEnrollmentFemale
## 186 96.1
## 187 NA
## 188 91.0
## 189 NA
## 190 95.1
## 194 NA
## 'data.frame': 139 obs. of 13 variables:
## $ Country : chr "Afghanistan" "Albania" "Antigua and Barbuda" "Austria" ...
## $ Region : chr "Eastern Mediterranean" "Europe" "Americas" "Europe" ...
## $ Population : int 29825 3162 89 8464 9309 372 155000 283 11060 324 ...
## $ Under15 : num 47.4 21.3 26 14.5 22.2 ...
## $ Over60 : num 3.82 14.93 12.35 23.52 8.24 ...
## $ FertilityRate : num 5.4 1.75 2.12 1.44 1.96 1.9 2.24 1.84 1.85 2.76 ...
## $ LifeExpectancy : int 60 74 75 81 71 75 70 78 80 74 ...
## $ ChildMortality : num 98.5 16.7 9.9 4 35.2 16.9 40.9 18.4 4.2 18.3 ...
## $ CellularSubscribers : num 54.3 96.4 196.4 154.8 108.8 ...
## $ LiteracyRate : num NA NA 99 NA NA NA 56.8 NA NA NA ...
## $ GNI : num 1140 8820 17900 42050 8960 ...
## $ PrimarySchoolEnrollmentMale : num NA NA 91.1 NA 85.3 NA NA NA 98.9 NA ...
## $ PrimarySchoolEnrollmentFemale: num NA NA 84.5 NA 84.1 NA NA NA 99.2 NA ...
## - attr(*, "comment")= chr "glb_trnobs_df"if (nrow(glb_trnobs_df) == nrow(glb_allobs_df))
warning("glb_trnobs_df same as glb_allobs_df")
if (nrow(glb_newobs_df) == nrow(glb_allobs_df))
warning("glb_newobs_df same as glb_allobs_df")
if (length(glb_drop_vars) > 0) {
warning("dropping vars: ", paste0(glb_drop_vars, collapse=", "))
glb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df), glb_drop_vars)]
glb_trnobs_df <- glb_trnobs_df[, setdiff(names(glb_trnobs_df), glb_drop_vars)]
glb_newobs_df <- glb_newobs_df[, setdiff(names(glb_newobs_df), glb_drop_vars)]
}
# Check for duplicates in glb_id_vars
if (length(glb_id_vars) == 0) {
warning("using .rownames as identifiers for observations")
glb_allobs_df$.rownames <- rownames(glb_allobs_df)
glb_id_vars <- ".rownames"
}
if (sum(duplicated(glb_allobs_df[, glb_id_vars, FALSE])) > 0)
stop(glb_id_vars, " duplicated in glb_allobs_df")
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_id_vars)
# Combine trnent & newent into glb_allobs_df for easier manipulation
glb_trnobs_df$.src <- "Train"; glb_newobs_df$.src <- "Test";
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, ".src")
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df)
comment(glb_allobs_df) <- "glb_allobs_df"
glb_trnobs_df <- glb_newobs_df <- NULL
glb_chunks_df <- myadd_chunk(glb_chunks_df, "inspect.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 1 import.data 1 0 7.716 8.259 0.544
## 2 inspect.data 2 0 8.260 NA NAStep 2.0: inspect data
#print(str(glb_allobs_df))
#View(glb_allobs_df)
dsp_class_dstrb <- function(var) {
xtab_df <- mycreate_xtab_df(glb_allobs_df, c(".src", var))
rownames(xtab_df) <- xtab_df$.src
xtab_df <- subset(xtab_df, select=-.src)
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
dsp_problem_data <- function(df, terminate=FALSE) {
print(sprintf("numeric data missing in %s: ",
ifelse(!is.null(df_name <- comment(df)), df_name, "")))
numeric_missing <- sapply(setdiff(names(df), myfind_chr_cols_df(df)),
function(col) sum(is.na(df[, col])))
numeric_missing <- numeric_missing[numeric_missing > 0]
print(numeric_missing)
numeric_feats_missing <- setdiff(names(numeric_missing), glb_exclude_vars_as_features)
if ((length(numeric_feats_missing) > 0) && terminate)
stop("terminating due to missing values")
print(sprintf("numeric data w/ 0s in %s: ",
ifelse(!is.null(df_name <- comment(df)), df_name, "")))
print(sapply(setdiff(names(df), myfind_chr_cols_df(df)),
function(col) sum(df[, col] == 0, na.rm=TRUE)))
print(sprintf("numeric data w/ Infs in %s: ",
ifelse(!is.null(df_name <- comment(df)), df_name, "")))
print(sapply(setdiff(names(df), myfind_chr_cols_df(df)),
function(col) sum(df[, col] == Inf, na.rm=TRUE)))
print(sprintf("numeric data w/ NaNs in %s: ",
ifelse(!is.null(df_name <- comment(df)), df_name, "")))
print(sapply(setdiff(names(df), myfind_chr_cols_df(df)),
function(col) sum(df[, col] == NaN, na.rm=TRUE)))
print(sprintf("string data missing in %s: ",
ifelse(!is.null(df_name <- comment(df)), df_name, "")))
print(sapply(setdiff(myfind_chr_cols_df(df), ".src"),
function(col) sum(df[, col] == "")))
}
# Performed repeatedly in other chunks
glb_chk_data <- function() {
# Histogram of predictor in glb_trnobs_df & glb_newobs_df
print(myplot_histogram(glb_allobs_df, glb_rsp_var_raw) + facet_wrap(~ .src))
if (glb_is_classification)
dsp_class_dstrb(var=ifelse(glb_rsp_var %in% names(glb_allobs_df),
glb_rsp_var, glb_rsp_var_raw))
dsp_problem_data(glb_allobs_df)
}
glb_chk_data()## Warning in loop_apply(n, do.ply): position_stack requires constant width:
## output may be incorrect## Warning in loop_apply(n, do.ply): position_stack requires constant width:
## output may be incorrect
## [1] "numeric data missing in glb_allobs_df: "
## FertilityRate CellularSubscribers
## 11 10
## LiteracyRate GNI
## 91 32
## PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 93 93
## [1] "numeric data w/ 0s in glb_allobs_df: "
## Population Under15
## 0 0
## Over60 FertilityRate
## 0 0
## LifeExpectancy ChildMortality
## 0 0
## CellularSubscribers LiteracyRate
## 0 0
## GNI PrimarySchoolEnrollmentMale
## 0 0
## PrimarySchoolEnrollmentFemale
## 0
## [1] "numeric data w/ Infs in glb_allobs_df: "
## Population Under15
## 0 0
## Over60 FertilityRate
## 0 0
## LifeExpectancy ChildMortality
## 0 0
## CellularSubscribers LiteracyRate
## 0 0
## GNI PrimarySchoolEnrollmentMale
## 0 0
## PrimarySchoolEnrollmentFemale
## 0
## [1] "numeric data w/ NaNs in glb_allobs_df: "
## Population Under15
## 0 0
## Over60 FertilityRate
## 0 0
## LifeExpectancy ChildMortality
## 0 0
## CellularSubscribers LiteracyRate
## 0 0
## GNI PrimarySchoolEnrollmentMale
## 0 0
## PrimarySchoolEnrollmentFemale
## 0
## [1] "string data missing in glb_allobs_df: "
## Country Region
## 0 0# Create new features that help diagnostics
if (!is.null(glb_map_rsp_raw_to_var)) {
glb_allobs_df[, glb_rsp_var] <-
glb_map_rsp_raw_to_var(glb_allobs_df[, glb_rsp_var_raw])
mycheck_map_results(mapd_df=glb_allobs_df,
from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
if (glb_is_classification) dsp_class_dstrb(glb_rsp_var)
}
# Convert dates to numbers
# typically, dates come in as chars;
# so this must be done before converting chars to factors
myextract_dates_df <- function(df, vars, rsp_var) {
keep_feats <- c(NULL)
for (var in vars) {
dates_df <- data.frame(.date=strptime(df[, var], "%Y-%m-%d %H:%M:%S"))
dates_df[, rsp_var] <- df[, rsp_var]
dates_df[, paste0(var, ".POSIX")] <- dates_df$.date
dates_df[, paste0(var, ".year")] <- as.numeric(format(dates_df$.date, "%Y"))
dates_df[, paste0(var, ".year.fctr")] <- as.factor(format(dates_df$.date, "%Y"))
dates_df[, paste0(var, ".month")] <- as.numeric(format(dates_df$.date, "%m"))
dates_df[, paste0(var, ".month.fctr")] <- as.factor(format(dates_df$.date, "%m"))
dates_df[, paste0(var, ".date")] <- as.numeric(format(dates_df$.date, "%d"))
dates_df[, paste0(var, ".date.fctr")] <-
cut(as.numeric(format(dates_df$.date, "%d")), 5) # by month week
dates_df[, paste0(var, ".juliandate")] <- as.numeric(format(dates_df$.date, "%j"))
# wkday Sun=0; Mon=1; ...; Sat=6
dates_df[, paste0(var, ".wkday")] <- as.numeric(format(dates_df$.date, "%w"))
dates_df[, paste0(var, ".wkday.fctr")] <- as.factor(format(dates_df$.date, "%w"))
# Federal holidays 1.9., 13.10., 27.11., 25.12.
# NYState holidays 1.9., 13.10., 11.11., 27.11., 25.12.
months <- dates_df[, paste0(var, ".month")]
dates <- dates_df[, paste0(var, ".date")]
dates_df[, paste0(var, ".hlday")] <-
ifelse( ((months == 09) & (dates == 01)) |
((months == 10) & (dates == 13)) |
((months == 11) & (dates == 27)) |
((months == 12) & (dates == 25)) ,
1, 0)
dates_df[, paste0(var, ".wkend")] <- as.numeric(
(dates_df[, paste0(var, ".wkday")] %in% c(0, 6)) |
dates_df[, paste0(var, ".hlday")] )
dates_df[, paste0(var, ".hour")] <- as.numeric(format(dates_df$.date, "%H"))
dates_df[, paste0(var, ".hour.fctr")] <-
cut(as.numeric(format(dates_df$.date, "%H")), 3) # by work-shift
dates_df[, paste0(var, ".minute")] <- as.numeric(format(dates_df$.date, "%M"))
dates_df[, paste0(var, ".minute.fctr")] <-
cut(as.numeric(format(dates_df$.date, "%M")), 4) # by quarter-hours
dates_df[, paste0(var, ".second")] <- as.numeric(format(dates_df$.date, "%S"))
dates_df[, paste0(var, ".second.fctr")] <-
cut(as.numeric(format(dates_df$.date, "%S")), 4) # by quarter-hours
dates_df[, paste0(var, ".day.minutes")] <- 60 * dates_df[, paste0(var, ".hour")] +
dates_df[, paste0(var, ".minute")]
dates_df[, paste0(var, ".day.minutes.poly.", 1:5)] <-
as.matrix(poly(dates_df[, paste0(var, ".day.minutes")], 5))
# print(gp <- myplot_box(df=dates_df, ycol_names="PubDate.day.minutes",
# xcol_name=rsp_var))
# print(gp <- myplot_scatter(df=dates_df, xcol_name=".rownames",
# ycol_name="PubDate.day.minutes", colorcol_name=rsp_var))
# print(gp <- myplot_scatter(df=dates_df, xcol_name="PubDate.juliandate",
# ycol_name="PubDate.day.minutes.poly.1", colorcol_name=rsp_var))
# print(gp <- myplot_scatter(df=dates_df, xcol_name="PubDate.day.minutes",
# ycol_name="PubDate.day.minutes.poly.4", colorcol_name=rsp_var))
#
# print(gp <- myplot_scatter(df=dates_df, xcol_name="PubDate.juliandate",
# ycol_name="PubDate.day.minutes", colorcol_name=rsp_var, smooth=TRUE))
# print(gp <- myplot_scatter(df=dates_df, xcol_name="PubDate.juliandate",
# ycol_name="PubDate.day.minutes.poly.4", colorcol_name=rsp_var, smooth=TRUE))
# print(gp <- myplot_scatter(df=dates_df, xcol_name="PubDate.juliandate",
# ycol_name=c("PubDate.day.minutes", "PubDate.day.minutes.poly.4"),
# colorcol_name=rsp_var))
print(gp <- myplot_scatter(df=subset(dates_df, Popular.fctr=="Y"),
xcol_name="PubDate.juliandate",
ycol_name="PubDate.day.minutes", colorcol_name=rsp_var))
print(gp <- myplot_box(df=dates_df, ycol_names="PubDate.second",
xcol_name=rsp_var))
print(gp <- myplot_bar(df=dates_df, ycol_names="PubDate.second.fctr",
xcol_name=rsp_var, colorcol_name="PubDate.second.fctr"))
keep_feats <- union(keep_feats, paste(var,
c(".POSIX", ".year.fctr", ".month.fctr", ".date.fctr", ".wkday.fctr",
".wkend", ".hour.fctr", ".minute.fctr", ".second.fctr",
paste0(".day.minutes.poly.", 1:5)), sep=""))
}
#myprint_df(dates_df)
return(dates_df[, keep_feats])
}
if (!is.null(glb_date_vars)) {
glb_allobs_df <- cbind(glb_allobs_df,
myextract_dates_df(df=glb_allobs_df, vars=glb_date_vars, rsp_var=glb_rsp_var))
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
paste(glb_date_vars, c("", ".POSIX"), sep=""))
}
if (!is.null(glb_date_vars)) {
srt_allobs_df <- orderBy(~PubDate.POSIX, glb_allobs_df)
print(myplot_scatter(subset(srt_allobs_df,
PubDate.POSIX < strptime("2014-09-02", "%Y-%m-%d")),
xcol_name="PubDate.POSIX", ycol_name=glb_rsp_var,
colorcol_name=glb_rsp_var
))
# Create features that measure the gap between previous timestamp in the data
require(zoo)
pd = as.POSIXlt(srt_allobs_df$PubDate)
z = zoo(as.numeric(pd))
srt_allobs_df[, "PubDate.zoo"] <- z
print(head(srt_allobs_df))
print(myplot_scatter(subset(srt_allobs_df,
PubDate.POSIX < strptime("2014-09-02", "%Y-%m-%d")),
xcol_name="PubDate.zoo", ycol_name=glb_rsp_var,
colorcol_name=glb_rsp_var
))
n = nrow(srt_allobs_df)
b = zoo(, seq(n))
last1 = as.numeric(merge(z-lag(z, -1), b, all = TRUE))
srt_allobs_df[, "PubDate.last1"] <- last1
srt_allobs_df[is.na(srt_allobs_df$PubDate.last1), "PubDate.last1"] <- 0
srt_allobs_df[, "PubDate.last1.log"] <- log(1 + srt_allobs_df[, "PubDate.last1"])
print(gp <- myplot_box(df=subset(srt_allobs_df, PubDate.last1.log > 0),
ycol_names="PubDate.last1.log",
xcol_name=glb_rsp_var))
last10 = as.numeric(merge(z-lag(z, -10), b, all = TRUE))
srt_allobs_df[, "PubDate.last10"] <- last10
srt_allobs_df[is.na(srt_allobs_df$PubDate.last10), "PubDate.last10"] <- 0
srt_allobs_df[, "PubDate.last10.log"] <- log(1 + srt_allobs_df[, "PubDate.last10"])
print(gp <- myplot_box(df=subset(srt_allobs_df, PubDate.last10.log > 0),
ycol_names="PubDate.last10.log",
xcol_name=glb_rsp_var))
last100 = as.numeric(merge(z-lag(z, -100), b, all = TRUE))
srt_allobs_df[, "PubDate.last100"] <- last100
srt_allobs_df[is.na(srt_allobs_df$PubDate.last100), "PubDate.last100"] <- 0
srt_allobs_df[, "PubDate.last100.log"] <- log(1 + srt_allobs_df[, "PubDate.last100"])
print(gp <- myplot_box(df=subset(srt_allobs_df, PubDate.last100.log > 0),
ycol_names="PubDate.last100.log",
xcol_name=glb_rsp_var))
glb_allobs_df <- srt_allobs_df
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c("PubDate.zoo", "PubDate.last1", "PubDate.last10", "PubDate.last100"))
# all2$last3 = as.numeric(merge(z-lag(z, -3), b, all = TRUE))
# all2$last5 = as.numeric(merge(z-lag(z, -5), b, all = TRUE))
# all2$last10 = as.numeric(merge(z-lag(z, -10), b, all = TRUE))
# all2$last20 = as.numeric(merge(z-lag(z, -20), b, all = TRUE))
# all2$last50 = as.numeric(merge(z-lag(z, -50), b, all = TRUE))
#
#
# # order table
# all2 = all2[order(all2$id),]
#
# ## fill in NAs
# # count averages
# na.avg = all2 %>% group_by(weekend, hour) %>% dplyr::summarise(
# last1=mean(last1, na.rm=TRUE),
# last3=mean(last3, na.rm=TRUE),
# last5=mean(last5, na.rm=TRUE),
# last10=mean(last10, na.rm=TRUE),
# last20=mean(last20, na.rm=TRUE),
# last50=mean(last50, na.rm=TRUE)
# )
#
# # fill in averages
# na.merge = merge(all2, na.avg, by=c("weekend","hour"))
# na.merge = na.merge[order(na.merge$id),]
# for(i in c("last1", "last3", "last5", "last10", "last20", "last50")) {
# y = paste0(i, ".y")
# idx = is.na(all2[[i]])
# all2[idx,][[i]] <- na.merge[idx,][[y]]
# }
# rm(na.avg, na.merge, b, i, idx, n, pd, sec, sh, y, z)
}
# check distribution of all numeric data
dsp_numeric_vars_dstrb <- function(vars_lst) {
for (var in vars_lst) {
print(sprintf("var: %s", var))
gp <- myplot_box(df=glb_allobs_df, ycol_names=var, xcol_name=glb_rsp_var)
if (inherits(glb_allobs_df[, var], "factor"))
gp <- gp + facet_wrap(reformulate(var))
print(gp)
}
}
# dsp_numeric_vars_dstrb(setdiff(names(glb_allobs_df),
# union(myfind_chr_cols_df(glb_allobs_df),
# c(glb_rsp_var_raw, glb_rsp_var))))
add_new_diag_feats <- function(obs_df, ref_df=glb_allobs_df) {
require(plyr)
obs_df <- mutate(obs_df,
# <col_name>.NA=is.na(<col_name>),
# <col_name>.fctr=factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# <col_name>.fctr=relevel(factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# "<ref_val>"),
# <col2_name>.fctr=relevel(factor(ifelse(<col1_name> == <val>, "<oth_val>", "<ref_val>")),
# as.factor(c("R", "<ref_val>")),
# ref="<ref_val>"),
# This doesn't work - use sapply instead
# <col_name>.fctr_num=grep(<col_name>, levels(<col_name>.fctr)),
#
# Date.my=as.Date(strptime(Date, "%m/%d/%y %H:%M")),
# Year=year(Date.my),
# Month=months(Date.my),
# Weekday=weekdays(Date.my)
# <col_name>=<table>[as.character(<col2_name>)],
# <col_name>=as.numeric(<col2_name>),
.rnorm=rnorm(n=nrow(obs_df))
)
if (!is.null(glb_log_vars)) {
stop("not implemented yet")
# Cycle thru glb_log_vars & create logs
# <col_name>.log=log(1 + <col.name>),
# Add raw_vars to glb_exclude_vars_as_features
# Add WordCount.log since WordCount is not distributed normally -> automatically do ???
print("Replacing WordCount with WordCount.log in potential feature set")
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, "WordCount")
}
# If levels of a factor are different across obs_df & glb_newobs_df; predict.glm fails
# Transformations not handled by mutate
# obs_df$<col_name>.fctr.num <- sapply(1:nrow(obs_df),
# function(row_ix) grep(obs_df[row_ix, "<col_name>"],
# levels(obs_df[row_ix, "<col_name>.fctr"])))
#print(summary(obs_df))
#print(sapply(names(obs_df), function(col) sum(is.na(obs_df[, col]))))
return(obs_df)
}
glb_allobs_df <- add_new_diag_feats(glb_allobs_df)## Loading required package: plyr# Check distributions of newly transformed / extracted vars
# Enhancement: remove vars that were displayed ealier
dsp_numeric_vars_dstrb(setdiff(names(glb_allobs_df),
union(myfind_chr_cols_df(glb_allobs_df),
union(glb_rsp_var_raw,
union(glb_rsp_var, glb_exclude_vars_as_features)))))## [1] "var: Population"## Warning in myplot_box(df = glb_allobs_df, ycol_names = var, xcol_name
## = glb_rsp_var): xcol_name:LifeExpectancy is not a factor; creating
## LifeExpectancy_fctr## [1] "var: Under15"## Warning in myplot_box(df = glb_allobs_df, ycol_names = var, xcol_name
## = glb_rsp_var): xcol_name:LifeExpectancy is not a factor; creating
## LifeExpectancy_fctr
## [1] "var: Over60"## Warning in myplot_box(df = glb_allobs_df, ycol_names = var, xcol_name
## = glb_rsp_var): xcol_name:LifeExpectancy is not a factor; creating
## LifeExpectancy_fctr
## [1] "var: FertilityRate"## Warning in myplot_box(df = glb_allobs_df, ycol_names = var, xcol_name
## = glb_rsp_var): xcol_name:LifeExpectancy is not a factor; creating
## LifeExpectancy_fctr
## Warning in loop_apply(n, do.ply): Removed 11 rows containing non-finite
## values (stat_boxplot).## Warning in loop_apply(n, do.ply): Removed 11 rows containing missing values
## (stat_summary).## Warning in loop_apply(n, do.ply): Removed 8 rows containing missing values
## (geom_text).## [1] "var: ChildMortality"## Warning in myplot_box(df = glb_allobs_df, ycol_names = var, xcol_name
## = glb_rsp_var): xcol_name:LifeExpectancy is not a factor; creating
## LifeExpectancy_fctr
## [1] "var: CellularSubscribers"## Warning in myplot_box(df = glb_allobs_df, ycol_names = var, xcol_name
## = glb_rsp_var): xcol_name:LifeExpectancy is not a factor; creating
## LifeExpectancy_fctr
## Warning in loop_apply(n, do.ply): Removed 10 rows containing non-finite
## values (stat_boxplot).## Warning in loop_apply(n, do.ply): Removed 10 rows containing missing values
## (stat_summary).## Warning in loop_apply(n, do.ply): Removed 9 rows containing missing values
## (geom_text).## [1] "var: LiteracyRate"## Warning in myplot_box(df = glb_allobs_df, ycol_names = var, xcol_name
## = glb_rsp_var): xcol_name:LifeExpectancy is not a factor; creating
## LifeExpectancy_fctr
## Warning in loop_apply(n, do.ply): Removed 91 rows containing non-finite
## values (stat_boxplot).## Warning in loop_apply(n, do.ply): Removed 91 rows containing missing values
## (stat_summary).## Warning in loop_apply(n, do.ply): Removed 28 rows containing missing values
## (geom_text).## [1] "var: GNI"## Warning in myplot_box(df = glb_allobs_df, ycol_names = var, xcol_name
## = glb_rsp_var): xcol_name:LifeExpectancy is not a factor; creating
## LifeExpectancy_fctr
## Warning in loop_apply(n, do.ply): Removed 32 rows containing non-finite
## values (stat_boxplot).## Warning in loop_apply(n, do.ply): Removed 32 rows containing missing values
## (stat_summary).## Warning in loop_apply(n, do.ply): Removed 19 rows containing missing values
## (geom_text).## [1] "var: PrimarySchoolEnrollmentMale"## Warning in myplot_box(df = glb_allobs_df, ycol_names = var, xcol_name
## = glb_rsp_var): xcol_name:LifeExpectancy is not a factor; creating
## LifeExpectancy_fctr
## Warning in loop_apply(n, do.ply): Removed 93 rows containing non-finite
## values (stat_boxplot).## Warning in loop_apply(n, do.ply): Removed 93 rows containing missing values
## (stat_summary).## Warning in loop_apply(n, do.ply): Removed 33 rows containing missing values
## (geom_text).## [1] "var: PrimarySchoolEnrollmentFemale"## Warning in myplot_box(df = glb_allobs_df, ycol_names = var, xcol_name
## = glb_rsp_var): xcol_name:LifeExpectancy is not a factor; creating
## LifeExpectancy_fctr
## Warning in loop_apply(n, do.ply): Removed 93 rows containing non-finite
## values (stat_boxplot).## Warning in loop_apply(n, do.ply): Removed 93 rows containing missing values
## (stat_summary).## Warning in loop_apply(n, do.ply): Removed 33 rows containing missing values
## (geom_text).## [1] "var: .rnorm"## Warning in myplot_box(df = glb_allobs_df, ycol_names = var, xcol_name
## = glb_rsp_var): xcol_name:LifeExpectancy is not a factor; creating
## LifeExpectancy_fctr
# Convert factors to dummy variables
# Build splines require(splines); bsBasis <- bs(training$age, df=3)
#pairs(subset(glb_trnobs_df, select=-c(col_symbol)))
# Check for glb_newobs_df & glb_trnobs_df features range mismatches
# Other diagnostics:
# print(subset(glb_trnobs_df, <col1_name> == max(glb_trnobs_df$<col1_name>, na.rm=TRUE) &
# <col2_name> <= mean(glb_trnobs_df$<col1_name>, na.rm=TRUE)))
# print(glb_trnobs_df[which.max(glb_trnobs_df$<col_name>),])
# print(<col_name>_freq_glb_trnobs_df <- mycreate_tbl_df(glb_trnobs_df, "<col_name>"))
# print(which.min(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>)[, 2]))
# print(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>))
# print(table(is.na(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(table(sign(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(mycreate_xtab_df(glb_trnobs_df, <col1_name>))
# print(mycreate_xtab_df(glb_trnobs_df, c(<col1_name>, <col2_name>)))
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mycreate_xtab_df(glb_trnobs_df, c("<col1_name>", "<col2_name>")))
# <col1_name>_<col2_name>_xtab_glb_trnobs_df[is.na(<col1_name>_<col2_name>_xtab_glb_trnobs_df)] <- 0
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mutate(<col1_name>_<col2_name>_xtab_glb_trnobs_df,
# <col3_name>=(<col1_name> * 1.0) / (<col1_name> + <col2_name>)))
# print(<col2_name>_min_entity_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>, min, na.rm=TRUE)))
# print(<col1_name>_na_by_<col2_name>_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>.NA, glb_trnobs_df$<col2_name>, mean, na.rm=TRUE)))
# Other plots:
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>"))
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>", xcol_name="<col2_name>"))
# print(myplot_line(subset(glb_trnobs_df, Symbol %in% c("KO", "PG")),
# "Date.my", "StockPrice", facet_row_colnames="Symbol") +
# geom_vline(xintercept=as.numeric(as.Date("2003-03-01"))) +
# geom_vline(xintercept=as.numeric(as.Date("1983-01-01")))
# )
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", colorcol_name="<Pred.fctr>") +
# geom_point(data=subset(glb_allobs_df, <condition>),
# mapping=aes(x=<x_var>, y=<y_var>), color="red", shape=4, size=5))
rm(srt_allobs_df, last1, last10, last100, pd)## Warning in rm(srt_allobs_df, last1, last10, last100, pd): object
## 'srt_allobs_df' not found## Warning in rm(srt_allobs_df, last1, last10, last100, pd): object 'last1'
## not found## Warning in rm(srt_allobs_df, last1, last10, last100, pd): object 'last10'
## not found## Warning in rm(srt_allobs_df, last1, last10, last100, pd): object 'last100'
## not found## Warning in rm(srt_allobs_df, last1, last10, last100, pd): object 'pd' not
## foundglb_chunks_df <- myadd_chunk(glb_chunks_df, "cleanse.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 2 inspect.data 2 0 8.260 22.189 13.929
## 3 cleanse.data 2 1 22.189 NA NAStep 2.1: cleanse data
# Options:
# 1. Not fill missing vars
# 2. Fill missing numerics with a different algorithm
# 3. Fill missing chars with data based on clusters
dsp_problem_data(glb_allobs_df)## [1] "numeric data missing in glb_allobs_df: "
## FertilityRate CellularSubscribers
## 11 10
## LiteracyRate GNI
## 91 32
## PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 93 93
## [1] "numeric data w/ 0s in glb_allobs_df: "
## Population Under15
## 0 0
## Over60 FertilityRate
## 0 0
## LifeExpectancy ChildMortality
## 0 0
## CellularSubscribers LiteracyRate
## 0 0
## GNI PrimarySchoolEnrollmentMale
## 0 0
## PrimarySchoolEnrollmentFemale .rnorm
## 0 0
## [1] "numeric data w/ Infs in glb_allobs_df: "
## Population Under15
## 0 0
## Over60 FertilityRate
## 0 0
## LifeExpectancy ChildMortality
## 0 0
## CellularSubscribers LiteracyRate
## 0 0
## GNI PrimarySchoolEnrollmentMale
## 0 0
## PrimarySchoolEnrollmentFemale .rnorm
## 0 0
## [1] "numeric data w/ NaNs in glb_allobs_df: "
## Population Under15
## 0 0
## Over60 FertilityRate
## 0 0
## LifeExpectancy ChildMortality
## 0 0
## CellularSubscribers LiteracyRate
## 0 0
## GNI PrimarySchoolEnrollmentMale
## 0 0
## PrimarySchoolEnrollmentFemale .rnorm
## 0 0
## [1] "string data missing in glb_allobs_df: "
## Country Region
## 0 0if (!is.null(glb_force_0_to_NA_vars)) {
warning("Forcing ", nrow(subset(glb_allobs_df, WordCount.log == 0)),
" obs with WordCount.log 0s to NA")
glb_allobs_df[glb_allobs_df$WordCount.log == 0, "WordCount.log"] <- NA
}
dsp_problem_data(glb_allobs_df)## [1] "numeric data missing in glb_allobs_df: "
## FertilityRate CellularSubscribers
## 11 10
## LiteracyRate GNI
## 91 32
## PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 93 93
## [1] "numeric data w/ 0s in glb_allobs_df: "
## Population Under15
## 0 0
## Over60 FertilityRate
## 0 0
## LifeExpectancy ChildMortality
## 0 0
## CellularSubscribers LiteracyRate
## 0 0
## GNI PrimarySchoolEnrollmentMale
## 0 0
## PrimarySchoolEnrollmentFemale .rnorm
## 0 0
## [1] "numeric data w/ Infs in glb_allobs_df: "
## Population Under15
## 0 0
## Over60 FertilityRate
## 0 0
## LifeExpectancy ChildMortality
## 0 0
## CellularSubscribers LiteracyRate
## 0 0
## GNI PrimarySchoolEnrollmentMale
## 0 0
## PrimarySchoolEnrollmentFemale .rnorm
## 0 0
## [1] "numeric data w/ NaNs in glb_allobs_df: "
## Population Under15
## 0 0
## Over60 FertilityRate
## 0 0
## LifeExpectancy ChildMortality
## 0 0
## CellularSubscribers LiteracyRate
## 0 0
## GNI PrimarySchoolEnrollmentMale
## 0 0
## PrimarySchoolEnrollmentFemale .rnorm
## 0 0
## [1] "string data missing in glb_allobs_df: "
## Country Region
## 0 0dsp_catgs <- function() {
print("NewsDesk:")
print(table(glb_allobs_df$NewsDesk))
print("SectionName:")
print(table(glb_allobs_df$SectionName))
print("SubsectionName:")
print(table(glb_allobs_df$SubsectionName))
}
sel_obs <- function(Popular=NULL,
NewsDesk=NULL, SectionName=NULL, SubsectionName=NULL,
Headline.contains=NULL, Snippet.contains=NULL, Abstract.contains=NULL,
Headline.pfx=NULL, NewsDesk.nb=NULL, .clusterid=NULL, myCategory=NULL,
perl=FALSE) {
tmp_df <- glb_allobs_df
# Does not work for Popular == NAs ???
if (!is.null(Popular)) {
if (is.na(Popular))
tmp_df <- tmp_df[is.na(tmp_df$Popular), ] else
tmp_df <- tmp_df[tmp_df$Popular == Popular, ]
}
if (!is.null(NewsDesk))
tmp_df <- tmp_df[tmp_df$NewsDesk == NewsDesk, ]
if (!is.null(SectionName))
tmp_df <- tmp_df[tmp_df$SectionName == SectionName, ]
if (!is.null(SubsectionName))
tmp_df <- tmp_df[tmp_df$SubsectionName == SubsectionName, ]
if (!is.null(Headline.contains))
tmp_df <-
tmp_df[grep(Headline.contains, tmp_df$Headline, perl=perl), ]
if (!is.null(Snippet.contains))
tmp_df <-
tmp_df[grep(Snippet.contains, tmp_df$Snippet, perl=perl), ]
if (!is.null(Abstract.contains))
tmp_df <-
tmp_df[grep(Abstract.contains, tmp_df$Abstract, perl=perl), ]
if (!is.null(Headline.pfx)) {
if (length(grep("Headline.pfx", names(tmp_df), fixed=TRUE, value=TRUE))
> 0) tmp_df <-
tmp_df[tmp_df$Headline.pfx == Headline.pfx, ] else
warning("glb_allobs_df does not contain Headline.pfx; ignoring that filter")
}
if (!is.null(NewsDesk.nb)) {
if (any(grepl("NewsDesk.nb", names(tmp_df), fixed=TRUE)) > 0)
tmp_df <-
tmp_df[tmp_df$NewsDesk.nb == NewsDesk.nb, ] else
warning("glb_allobs_df does not contain NewsDesk.nb; ignoring that filter")
}
if (!is.null(.clusterid)) {
if (any(grepl(".clusterid", names(tmp_df), fixed=TRUE)) > 0)
tmp_df <-
tmp_df[tmp_df$clusterid == clusterid, ] else
warning("glb_allobs_df does not contain clusterid; ignoring that filter") }
if (!is.null(myCategory)) {
if (!(myCategory %in% names(glb_allobs_df)))
tmp_df <-
tmp_df[tmp_df$myCategory == myCategory, ] else
warning("glb_allobs_df does not contain myCategory; ignoring that filter")
}
return(glb_allobs_df$UniqueID %in% tmp_df$UniqueID)
}
dsp_obs <- function(..., cols=c(NULL), all=FALSE) {
tmp_df <- glb_allobs_df[sel_obs(...),
union(c("UniqueID", "Popular", "myCategory", "Headline"), cols), FALSE]
if(all) { print(tmp_df) } else { myprint_df(tmp_df) }
}
#dsp_obs(Popular=1, NewsDesk="", SectionName="", Headline.contains="Boehner")
# dsp_obs(Popular=1, NewsDesk="", SectionName="")
# dsp_obs(Popular=NA, NewsDesk="", SectionName="")
dsp_tbl <- function(...) {
tmp_entity_df <- glb_allobs_df[sel_obs(...), ]
tmp_tbl <- table(tmp_entity_df$NewsDesk,
tmp_entity_df$SectionName,
tmp_entity_df$SubsectionName,
tmp_entity_df$Popular, useNA="ifany")
#print(names(tmp_tbl))
#print(dimnames(tmp_tbl))
print(tmp_tbl)
}
dsp_hdlxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "Headline", glb_rsp_var)))
#dsp_hdlxtab("(1914)|(1939)")
dsp_catxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# dsp_catxtab("1914)|(1939)")
# dsp_catxtab("19(14|39|64):")
# dsp_catxtab("19..:")
# Create myCategory <- NewsDesk#SectionName#SubsectionName
# Fix some data before merging categories
# glb_allobs_df[sel_obs(Headline.contains="Your Turn:", NewsDesk=""),
# "NewsDesk"] <- "Styles"
# glb_allobs_df[sel_obs(Headline.contains="School", NewsDesk="", SectionName="U.S.",
# SubsectionName=""),
# "SubsectionName"] <- "Education"
# glb_allobs_df[sel_obs(Headline.contains="Today in Small Business:", NewsDesk="Business"),
# "SectionName"] <- "Business Day"
# glb_allobs_df[sel_obs(Headline.contains="Today in Small Business:", NewsDesk="Business"),
# "SubsectionName"] <- "Small Business"
# glb_allobs_df[sel_obs(Headline.contains="Readers Respond:"),
# "SectionName"] <- "Opinion"
# glb_allobs_df[sel_obs(Headline.contains="Readers Respond:"),
# "SubsectionName"] <- "Room For Debate"
# glb_allobs_df[sel_obs(NewsDesk="Business", SectionName="", SubsectionName="", Popular=NA),
# "SubsectionName"] <- "Small Business"
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% c(7973),
# c("UniqueID", "Headline", "myCategory", "NewsDesk", "SectionName", "SubsectionName")])
#
# glb_allobs_df[sel_obs(NewsDesk="Business", SectionName="", SubsectionName=""),
# "SectionName"] <- "Technology"
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% c(5076, 5736, 5924, 5911, 6532),
# c("UniqueID", "Headline", "myCategory", "NewsDesk", "SectionName", "SubsectionName")])
#
# glb_allobs_df[sel_obs(SectionName="Health"),
# "NewsDesk"] <- "Science"
# glb_allobs_df[sel_obs(SectionName="Travel"),
# "NewsDesk"] <- "Travel"
#
# glb_allobs_df[sel_obs(SubsectionName="Fashion & Style"),
# "SectionName"] <- ""
# glb_allobs_df[sel_obs(SubsectionName="Fashion & Style"),
# "SubsectionName"] <- ""
# glb_allobs_df[sel_obs(NewsDesk="Styles", SectionName="", SubsectionName="", Popular=1),
# "SectionName"] <- "U.S."
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% c(5486),
# c("UniqueID", "Headline", "myCategory", "NewsDesk", "SectionName", "SubsectionName")])
#
# glb_allobs_df$myCategory <- paste(glb_allobs_df$NewsDesk,
# glb_allobs_df$SectionName,
# glb_allobs_df$SubsectionName,
# sep="#")
# dsp_obs( Headline.contains="Music:"
# #,NewsDesk=""
# #,SectionName=""
# #,SubsectionName="Fashion & Style"
# #,Popular=1 #NA
# ,cols= c("UniqueID", "Headline", "Popular", "myCategory",
# "NewsDesk", "SectionName", "SubsectionName"),
# all=TRUE)
# dsp_obs( Headline.contains="."
# ,NewsDesk=""
# ,SectionName="Opinion"
# ,SubsectionName=""
# #,Popular=1 #NA
# ,cols= c("UniqueID", "Headline", "Popular", "myCategory",
# "NewsDesk", "SectionName", "SubsectionName"),
# all=TRUE)
# Merge some categories
# glb_allobs_df$myCategory <-
# plyr::revalue(glb_allobs_df$myCategory, c(
# "#Business Day#Dealbook" = "Business#Business Day#Dealbook",
# "#Business Day#Small Business" = "Business#Business Day#Small Business",
# "#Crosswords/Games#" = "Business#Crosswords/Games#",
# "Business##" = "Business#Technology#",
# "#Open#" = "Business#Technology#",
# "#Technology#" = "Business#Technology#",
#
# "#Arts#" = "Culture#Arts#",
# "Culture##" = "Culture#Arts#",
#
# "#World#Asia Pacific" = "Foreign#World#Asia Pacific",
# "Foreign##" = "Foreign#World#",
#
# "#N.Y. / Region#" = "Metro#N.Y. / Region#",
#
# "#Opinion#" = "OpEd#Opinion#",
# "OpEd##" = "OpEd#Opinion#",
#
# "#Health#" = "Science#Health#",
# "Science##" = "Science#Health#",
#
# "Styles##" = "Styles##Fashion",
# "Styles#Health#" = "Science#Health#",
# "Styles#Style#Fashion & Style" = "Styles##Fashion",
#
# "#Travel#" = "Travel#Travel#",
#
# "Magazine#Magazine#" = "myOther",
# "National##" = "myOther",
# "National#U.S.#Politics" = "myOther",
# "Sports##" = "myOther",
# "Sports#Sports#" = "myOther",
# "#U.S.#" = "myOther",
#
#
# # "Business##Small Business" = "Business#Business Day#Small Business",
# #
# # "#Opinion#" = "#Opinion#Room For Debate",
# "##" = "##"
# # "Business##" = "Business#Business Day#Dealbook",
# # "Foreign#World#" = "Foreign##",
# # "#Open#" = "Other",
# # "#Opinion#The Public Editor" = "OpEd#Opinion#",
# # "Styles#Health#" = "Styles##",
# # "Styles#Style#Fashion & Style" = "Styles##",
# # "#U.S.#" = "#U.S.#Education",
# ))
# ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
# mycreate_sqlxtab_df(glb_allobs_df,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# myprint_df(ctgry_xtab_df)
# write.table(ctgry_xtab_df, paste0(glb_out_pfx, "ctgry_xtab.csv"),
# row.names=FALSE)
# ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
# myCategory + NewsDesk + SectionName + SubsectionName ~
# Popular.fctr, sum, value.var=".n"))
# myprint_df(ctgry_cast_df)
# write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_cast.csv"),
# row.names=FALSE)
# print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df[, glb_rsp_var],
# useNA="ifany"))
dsp_chisq.test <- function(...) {
sel_df <- glb_allobs_df[sel_obs(...) &
!is.na(glb_allobs_df$Popular), ]
sel_df$.marker <- 1
ref_df <- glb_allobs_df[!is.na(glb_allobs_df$Popular), ]
mrg_df <- merge(ref_df[, c(glb_id_vars, "Popular")],
sel_df[, c(glb_id_vars, ".marker")], all.x=TRUE)
mrg_df[is.na(mrg_df)] <- 0
print(mrg_tbl <- table(mrg_df$.marker, mrg_df$Popular))
print("Rows:Selected; Cols:Popular")
#print(mrg_tbl)
print(chisq.test(mrg_tbl))
}
# dsp_chisq.test(Headline.contains="[Ee]bola")
# dsp_chisq.test(Snippet.contains="[Ee]bola")
# dsp_chisq.test(Abstract.contains="[Ee]bola")
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola"), ],
# c(glb_rsp_var, "NewsDesk", "SectionName", "SubsectionName")))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName))
# print(table(glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# glb_allobs_df$myCategory.fctr <- as.factor(glb_allobs_df$myCategory)
# glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName"))
# Copy Headline into Snipper & Abstract if they are empty
# print(glb_allobs_df[nchar(glb_allobs_df[, "Snippet"]) == 0, c("Headline", "Snippet")])
# print(glb_allobs_df[glb_allobs_df$Headline == glb_allobs_df$Snippet,
# c("UniqueID", "Headline", "Snippet")])
# glb_allobs_df[nchar(glb_allobs_df[, "Snippet"]) == 0, "Snippet"] <-
# glb_allobs_df[nchar(glb_allobs_df[, "Snippet"]) == 0, "Headline"]
#
# print(glb_allobs_df[nchar(glb_allobs_df[, "Abstract"]) == 0, c("Headline", "Abstract")])
# print(glb_allobs_df[glb_allobs_df$Headline == glb_allobs_df$Abstract,
# c("UniqueID", "Headline", "Abstract")])
# glb_allobs_df[nchar(glb_allobs_df[, "Abstract"]) == 0, "Abstract"] <-
# glb_allobs_df[nchar(glb_allobs_df[, "Abstract"]) == 0, "Headline"]
# WordCount_0_df <- subset(glb_allobs_df, WordCount == 0)
# table(WordCount_0_df$Popular, WordCount_0_df$WordCount, useNA="ifany")
# myprint_df(WordCount_0_df[,
# c("UniqueID", "Popular", "WordCount", "Headline")])
glb_chunks_df <- myadd_chunk(glb_chunks_df, "manage.missing.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 3 cleanse.data 2 1 22.189 25.021 2.832
## 4 manage.missing.data 2 2 25.022 NA NAStep 2.2: manage missing data
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# glb_trnobs_df <- na.omit(glb_trnobs_df)
# glb_newobs_df <- na.omit(glb_newobs_df)
# df[is.na(df)] <- 0
dsp_problem_data(glb_allobs_df)## [1] "numeric data missing in glb_allobs_df: "
## FertilityRate CellularSubscribers
## 11 10
## LiteracyRate GNI
## 91 32
## PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 93 93
## [1] "numeric data w/ 0s in glb_allobs_df: "
## Population Under15
## 0 0
## Over60 FertilityRate
## 0 0
## LifeExpectancy ChildMortality
## 0 0
## CellularSubscribers LiteracyRate
## 0 0
## GNI PrimarySchoolEnrollmentMale
## 0 0
## PrimarySchoolEnrollmentFemale .rnorm
## 0 0
## [1] "numeric data w/ Infs in glb_allobs_df: "
## Population Under15
## 0 0
## Over60 FertilityRate
## 0 0
## LifeExpectancy ChildMortality
## 0 0
## CellularSubscribers LiteracyRate
## 0 0
## GNI PrimarySchoolEnrollmentMale
## 0 0
## PrimarySchoolEnrollmentFemale .rnorm
## 0 0
## [1] "numeric data w/ NaNs in glb_allobs_df: "
## Population Under15
## 0 0
## Over60 FertilityRate
## 0 0
## LifeExpectancy ChildMortality
## 0 0
## CellularSubscribers LiteracyRate
## 0 0
## GNI PrimarySchoolEnrollmentMale
## 0 0
## PrimarySchoolEnrollmentFemale .rnorm
## 0 0
## [1] "string data missing in glb_allobs_df: "
## Country Region
## 0 0# Not refactored into mydsutils.R since glb_*_df might be reassigned
glb_impute_missing_data <- function() {
require(mice)
set.seed(glb_mice_complete.seed)
inp_impent_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
union(glb_exclude_vars_as_features, glb_rsp_var))]
print("Summary before imputation: ")
print(summary(inp_impent_df))
out_impent_df <- complete(mice(inp_impent_df))
print(summary(out_impent_df))
ret_vars <- sapply(names(out_impent_df),
function(col) ifelse(!identical(out_impent_df[, col], inp_impent_df[, col]),
col, ""))
ret_vars <- ret_vars[ret_vars != ""]
return(out_impent_df[, ret_vars])
}
if (glb_impute_na_data &&
(ncol(nonna_df <- glb_impute_missing_data()) > 0)) {
for (col in names(nonna_df)) {
glb_allobs_df[, paste0(col, ".nonNA")] <- nonna_df[, col]
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, col)
}
} ## Loading required package: mice
## Loading required package: Rcpp
## mice 2.22 2014-06-10## [1] "Summary before imputation: "
## Region Population Under15 Over60
## Length:194 Min. : 1 Min. :13.12 Min. : 0.81
## Class :character 1st Qu.: 1696 1st Qu.:18.72 1st Qu.: 5.20
## Mode :character Median : 7790 Median :28.65 Median : 8.53
## Mean : 36360 Mean :28.73 Mean :11.16
## 3rd Qu.: 24535 3rd Qu.:37.75 3rd Qu.:16.69
## Max. :1390000 Max. :49.99 Max. :31.92
##
## FertilityRate ChildMortality CellularSubscribers LiteracyRate
## Min. :1.260 Min. : 2.200 Min. : 2.57 Min. :31.10
## 1st Qu.:1.835 1st Qu.: 8.425 1st Qu.: 63.57 1st Qu.:71.60
## Median :2.400 Median : 18.600 Median : 97.75 Median :91.80
## Mean :2.941 Mean : 36.149 Mean : 93.64 Mean :83.71
## 3rd Qu.:3.905 3rd Qu.: 55.975 3rd Qu.:120.81 3rd Qu.:97.85
## Max. :7.580 Max. :181.600 Max. :196.41 Max. :99.80
## NA's :11 NA's :10 NA's :91
## GNI PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## Min. : 340 Min. : 37.20 Min. : 32.50
## 1st Qu.: 2335 1st Qu.: 87.70 1st Qu.: 87.30
## Median : 7870 Median : 94.70 Median : 95.10
## Mean :13321 Mean : 90.85 Mean : 89.63
## 3rd Qu.:17558 3rd Qu.: 98.10 3rd Qu.: 97.90
## Max. :86440 Max. :100.00 Max. :100.00
## NA's :32 NA's :93 NA's :93
## .rnorm
## Min. :-2.05325
## 1st Qu.:-0.60703
## Median :-0.07994
## Mean : 0.01068
## 3rd Qu.: 0.61548
## Max. : 3.24104
##
##
## iter imp variable
## 1 1 FertilityRate CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 1 2 FertilityRate CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 1 3 FertilityRate CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 1 4 FertilityRate CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 1 5 FertilityRate CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 2 1 FertilityRate CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 2 2 FertilityRate CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 2 3 FertilityRate CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 2 4 FertilityRate CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 2 5 FertilityRate CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 3 1 FertilityRate CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 3 2 FertilityRate CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 3 3 FertilityRate CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 3 4 FertilityRate CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 3 5 FertilityRate CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 4 1 FertilityRate CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 4 2 FertilityRate CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 4 3 FertilityRate CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 4 4 FertilityRate CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 4 5 FertilityRate CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 5 1 FertilityRate CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 5 2 FertilityRate CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 5 3 FertilityRate CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 5 4 FertilityRate CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 5 5 FertilityRate CellularSubscribers LiteracyRate GNI PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## Region Population Under15 Over60
## Length:194 Min. : 1 Min. :13.12 Min. : 0.81
## Class :character 1st Qu.: 1696 1st Qu.:18.72 1st Qu.: 5.20
## Mode :character Median : 7790 Median :28.65 Median : 8.53
## Mean : 36360 Mean :28.73 Mean :11.16
## 3rd Qu.: 24535 3rd Qu.:37.75 3rd Qu.:16.69
## Max. :1390000 Max. :49.99 Max. :31.92
## FertilityRate ChildMortality CellularSubscribers LiteracyRate
## Min. :1.260 Min. : 2.200 Min. : 2.57 Min. :31.10
## 1st Qu.:1.840 1st Qu.: 8.425 1st Qu.: 65.00 1st Qu.:74.97
## Median :2.420 Median : 18.600 Median : 98.11 Median :89.55
## Mean :2.905 Mean : 36.149 Mean : 94.10 Mean :84.67
## 3rd Qu.:3.768 3rd Qu.: 55.975 3rd Qu.:121.38 3rd Qu.:97.28
## Max. :7.580 Max. :181.600 Max. :196.41 Max. :99.80
## GNI PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## Min. : 340 Min. : 37.20 Min. : 32.50
## 1st Qu.: 2652 1st Qu.: 86.50 1st Qu.: 85.60
## Median : 7235 Median : 93.50 Median : 94.40
## Mean :13497 Mean : 90.81 Mean : 89.66
## 3rd Qu.:16822 3rd Qu.: 97.70 3rd Qu.: 97.10
## Max. :86440 Max. :100.00 Max. :100.00
## .rnorm
## Min. :-2.05325
## 1st Qu.:-0.60703
## Median :-0.07994
## Mean : 0.01068
## 3rd Qu.: 0.61548
## Max. : 3.24104dsp_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in glb_allobs_df: "
## FertilityRate CellularSubscribers
## 11 10
## LiteracyRate GNI
## 91 32
## PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentFemale
## 93 93
## [1] "numeric data w/ 0s in glb_allobs_df: "
## Population Under15
## 0 0
## Over60 FertilityRate
## 0 0
## LifeExpectancy ChildMortality
## 0 0
## CellularSubscribers LiteracyRate
## 0 0
## GNI PrimarySchoolEnrollmentMale
## 0 0
## PrimarySchoolEnrollmentFemale .rnorm
## 0 0
## FertilityRate.nonNA CellularSubscribers.nonNA
## 0 0
## LiteracyRate.nonNA GNI.nonNA
## 0 0
## PrimarySchoolEnrollmentMale.nonNA PrimarySchoolEnrollmentFemale.nonNA
## 0 0
## [1] "numeric data w/ Infs in glb_allobs_df: "
## Population Under15
## 0 0
## Over60 FertilityRate
## 0 0
## LifeExpectancy ChildMortality
## 0 0
## CellularSubscribers LiteracyRate
## 0 0
## GNI PrimarySchoolEnrollmentMale
## 0 0
## PrimarySchoolEnrollmentFemale .rnorm
## 0 0
## FertilityRate.nonNA CellularSubscribers.nonNA
## 0 0
## LiteracyRate.nonNA GNI.nonNA
## 0 0
## PrimarySchoolEnrollmentMale.nonNA PrimarySchoolEnrollmentFemale.nonNA
## 0 0
## [1] "numeric data w/ NaNs in glb_allobs_df: "
## Population Under15
## 0 0
## Over60 FertilityRate
## 0 0
## LifeExpectancy ChildMortality
## 0 0
## CellularSubscribers LiteracyRate
## 0 0
## GNI PrimarySchoolEnrollmentMale
## 0 0
## PrimarySchoolEnrollmentFemale .rnorm
## 0 0
## FertilityRate.nonNA CellularSubscribers.nonNA
## 0 0
## LiteracyRate.nonNA GNI.nonNA
## 0 0
## PrimarySchoolEnrollmentMale.nonNA PrimarySchoolEnrollmentFemale.nonNA
## 0 0
## [1] "string data missing in glb_allobs_df: "
## Country Region
## 0 0glb_chunks_df <- myadd_chunk(glb_chunks_df, "encode.data", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 4 manage.missing.data 2 2 25.022 26.252 1.23
## 5 encode.data 2 3 26.252 NA NAStep 2.3: encode data
# map_<col_name>_df <- myimport_data(
# url="<map_url>",
# comment="map_<col_name>_df", print_diagn=TRUE)
# map_<col_name>_df <- read.csv(paste0(getwd(), "/data/<file_name>.csv"), strip.white=TRUE)
# glb_trnobs_df <- mymap_codes(glb_trnobs_df, "<from_col_name>", "<to_col_name>",
# map_<to_col_name>_df, map_join_col_name="<map_join_col_name>",
# map_tgt_col_name="<to_col_name>")
# glb_newobs_df <- mymap_codes(glb_newobs_df, "<from_col_name>", "<to_col_name>",
# map_<to_col_name>_df, map_join_col_name="<map_join_col_name>",
# map_tgt_col_name="<to_col_name>")
# glb_trnobs_df$<col_name>.fctr <- factor(glb_trnobs_df$<col_name>,
# as.factor(union(glb_trnobs_df$<col_name>, glb_newobs_df$<col_name>)))
# glb_newobs_df$<col_name>.fctr <- factor(glb_newobs_df$<col_name>,
# as.factor(union(glb_trnobs_df$<col_name>, glb_newobs_df$<col_name>)))
glb_chunks_df <- myadd_chunk(glb_chunks_df, "extract.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 5 encode.data 2 3 26.252 26.277 0.025
## 6 extract.features 3 0 26.277 NA NAStep 3.0: extract features
#```{r extract_features, cache=FALSE, eval=!is.null(glb_txt_vars)}
extract.features_chunk_df <- myadd_chunk(NULL, "extract.features_bgn")## label step_major step_minor bgn end elapsed
## 1 extract.features_bgn 1 0 26.318 NA NA# Create new features that help prediction
# <col_name>.lag.2 <- lag(zoo(glb_trnobs_df$<col_name>), -2, na.pad=TRUE)
# glb_trnobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
# <col_name>.lag.2 <- lag(zoo(glb_newobs_df$<col_name>), -2, na.pad=TRUE)
# glb_newobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
#
# glb_newobs_df[1, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df) - 1,
# "<col_name>"]
# glb_newobs_df[2, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df),
# "<col_name>"]
# glb_allobs_df <- mutate(glb_allobs_df,
# A.P.http=ifelse(grepl("http",Added,fixed=TRUE), 1, 0)
# )
#
# glb_trnobs_df <- mutate(glb_trnobs_df,
# )
#
# glb_newobs_df <- mutate(glb_newobs_df,
# )
# Create factors of string variables
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "factorize.str.vars"), major.inc=TRUE)## label step_major step_minor bgn end
## 1 extract.features_bgn 1 0 26.318 26.325
## 2 extract.features_factorize.str.vars 2 0 26.326 NA
## elapsed
## 1 0.007
## 2 NAprint(str_vars <- myfind_chr_cols_df(glb_allobs_df))## Country Region .src
## "Country" "Region" ".src"if (length(str_vars <- setdiff(str_vars,
glb_exclude_vars_as_features)) > 0) {
for (var in str_vars) {
warning("Creating factors of string variable: ", var,
": # of unique values: ", length(unique(glb_allobs_df[, var])))
glb_allobs_df[, paste0(var, ".fctr")] <- factor(glb_allobs_df[, var],
as.factor(unique(glb_allobs_df[, var])))
# glb_trnobs_df[, paste0(var, ".fctr")] <- factor(glb_trnobs_df[, var],
# as.factor(unique(glb_allobs_df[, var])))
# glb_newobs_df[, paste0(var, ".fctr")] <- factor(glb_newobs_df[, var],
# as.factor(unique(glb_allobs_df[, var])))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, str_vars)
}## Warning: Creating factors of string variable: Region: # of unique values: 6if (!is.null(glb_txt_vars)) {
require(foreach)
require(gsubfn)
require(stringr)
require(tm)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text"), major.inc=TRUE)
chk_pattern_freq <- function(re_str, ignore.case=TRUE) {
match_mtrx <- str_extract_all(txt_vctr, regex(re_str, ignore_case=ignore.case),
simplify=TRUE)
match_df <- as.data.frame(match_mtrx[match_mtrx != ""])
names(match_df) <- "pattern"
return(mycreate_sqlxtab_df(match_df, "pattern"))
}
#tmp_freq_df <- chk_pattern_freq("\\bNew (\\w)+", ignore.case=FALSE)
#subset(chk_pattern_freq("\\bNew (\\w)+", ignore.case=FALSE), grepl("New [[:upper:]]", pattern))
#chk_pattern_freq("\\bnew (\\W)+")
chk_subfn <- function(pos_ix) {
re_str <- gsubfn_args_lst[["re_str"]][[pos_ix]]
print("re_str:"); print(re_str)
rp_frmla <- gsubfn_args_lst[["rp_frmla"]][[pos_ix]]
print("rp_frmla:"); print(rp_frmla, showEnv=FALSE)
tmp_vctr <- grep(re_str, txt_vctr, value=TRUE, ignore.case=TRUE)[1:5]
print("Before:")
print(tmp_vctr)
print("After:")
print(gsubfn(re_str, rp_frmla, tmp_vctr, ignore.case=TRUE))
}
#chk_subfn(1)
myapply_gsub <- function(...) {
if ((length_lst <- length(names(gsub_map_lst))) == 0)
return(txt_vctr)
for (ptn_ix in 1:length_lst) {
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
length(names(gsub_map_lst)), names(gsub_map_lst)[ptn_ix]))
txt_vctr <- gsub(names(gsub_map_lst)[ptn_ix], gsub_map_lst[[ptn_ix]],
txt_vctr, ...)
}
return(txt_vctr)
}
myapply_txtmap <- function(txt_vctr, ...) {
nrows <- nrow(glb_txt_map_df)
for (ptn_ix in 1:nrows) {
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
nrows, glb_txt_map_df[ptn_ix, "rex_str"]))
txt_vctr <- gsub(glb_txt_map_df[ptn_ix, "rex_str"],
glb_txt_map_df[ptn_ix, "rpl_str"],
txt_vctr, ...)
}
return(txt_vctr)
}
chk.equal <- function(bgn, end) {
print(all.equal(sav_txt_lst[["Headline"]][bgn:end], glb_txt_lst[["Headline"]][bgn:end]))
}
dsp.equal <- function(bgn, end) {
print(sav_txt_lst[["Headline"]][bgn:end])
print(glb_txt_lst[["Headline"]][bgn:end])
}
#sav_txt_lst <- glb_txt_lst; all.equal(sav_txt_lst, glb_txt_lst)
#all.equal(sav_txt_lst[["Headline"]][1:4200], glb_txt_lst[["Headline"]][1:4200])
#all.equal(sav_txt_lst[["Headline"]][1:2000], glb_txt_lst[["Headline"]][1:2000])
#all.equal(sav_txt_lst[["Headline"]][1:1000], glb_txt_lst[["Headline"]][1:1000])
#all.equal(sav_txt_lst[["Headline"]][1:500], glb_txt_lst[["Headline"]][1:500])
#all.equal(sav_txt_lst[["Headline"]][1:200], glb_txt_lst[["Headline"]][1:200])
#all.equal(sav_txt_lst[["Headline"]][1:100], glb_txt_lst[["Headline"]][1:100])
#chk.equal( 1, 100)
#chk.equal(51, 100)
#chk.equal(81, 100)
#chk.equal(81, 90)
#chk.equal(81, 85)
#chk.equal(86, 90)
#chk.equal(96, 100)
#dsp.equal(86, 90)
glb_txt_map_df <- read.csv("mytxt_map.csv", comment.char="#", strip.white=TRUE)
glb_txt_lst <- list();
print(sprintf("Building glb_txt_lst..."))
glb_txt_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_vctr <- glb_allobs_df[, txt_var]
# myapply_txtmap shd be created as a tm_map::content_transformer ?
#print(glb_txt_map_df)
#txt_var=glb_txt_vars[3]; txt_vctr <- glb_txt_lst[[txt_var]]
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rex_str == "\\bWall St\\.", "rex_str"])
#print(rex_str <- glb_txt_map_df[grepl("du Pont", glb_txt_map_df$rex_str), "rex_str"])
#print(tmp_vctr <- grep(rex_str, txt_vctr, value=TRUE, ignore.case=FALSE))
#ret_lst <- regexec(rex_str, txt_vctr, ignore.case=FALSE); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
#gsub(rex_str, glb_txt_map_df[glb_txt_map_df$rex_str == rex_str, "rpl_str"], tmp_vctr, ignore.case=FALSE)
#grep("Hong Hong", txt_vctr, value=TRUE)
txt_vctr <- myapply_txtmap(txt_vctr, ignore.case=FALSE)
}
names(glb_txt_lst) <- glb_txt_vars
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining Acronyms in %s:", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(tmp_vctr <- grep("[[:upper:]]\\.", txt_vctr, value=TRUE, ignore.case=FALSE))
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl("( |-)[[:upper:]]", pattern))))
print(" consider cleaning if relevant to problem domain; geography name; .n > 1")
#grep("New G", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Wins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(N|S|E|W|C)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))))
#grep("N Weaver", txt_vctr, value=TRUE, ignore.case=FALSE)
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(North|South|East|West|Central)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))))
#grep("Central (African|Bankers|Cast|Italy|Role|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("East (Africa|Berlin|London|Poland|Rivals|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("North (American|Korean|West)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("South (Pacific|Street)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Martins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
find_cmpnd_wrds <- function(txt_vctr) {
txt_corpus <- Corpus(VectorSource(txt_vctr))
txt_corpus <- tm_map(txt_corpus, tolower)
txt_corpus <- tm_map(txt_corpus, PlainTextDocument)
txt_corpus <- tm_map(txt_corpus, removePunctuation,
preserve_intra_word_dashes=TRUE)
full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTf))
print(" Full TermMatrix:"); print(full_Tf_DTM)
full_Tf_mtrx <- as.matrix(full_Tf_DTM)
rownames(full_Tf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_Tf_vctr <- colSums(full_Tf_mtrx)
names(full_Tf_vctr) <- dimnames(full_Tf_DTM)[[2]]
#grep("year", names(full_Tf_vctr), value=TRUE)
#which.max(full_Tf_mtrx[, "yearlong"])
full_Tf_df <- as.data.frame(full_Tf_vctr)
names(full_Tf_df) <- "Tf.full"
full_Tf_df$term <- rownames(full_Tf_df)
#full_Tf_df$freq.full <- colSums(full_Tf_mtrx != 0)
full_Tf_df <- orderBy(~ -Tf.full, full_Tf_df)
cmpnd_Tf_df <- full_Tf_df[grep("-", full_Tf_df$term, value=TRUE) ,]
filter_df <- read.csv("mytxt_compound.csv", comment.char="#", strip.white=TRUE)
cmpnd_Tf_df$filter <- FALSE
for (row_ix in 1:nrow(filter_df))
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "filter"] <-
grepl(filter_df[row_ix, "rex_str"],
cmpnd_Tf_df[!cmpnd_Tf_df$filter, "term"], ignore.case=TRUE)
cmpnd_Tf_df <- subset(cmpnd_Tf_df, !filter)
# Bug in tm_map(txt_corpus, removePunctuation, preserve_intra_word_dashes=TRUE) ???
# "net-a-porter" gets converted to "net-aporter"
#grep("net-a-porter", txt_vctr, ignore.case=TRUE, value=TRUE)
#grep("maser-laser", txt_vctr, ignore.case=TRUE, value=TRUE)
#txt_corpus[[which(grepl("net-a-porter", txt_vctr, ignore.case=TRUE))]]
#grep("\\b(across|longer)-(\\w)", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
#grep("(\\w)-(affected|term)\\b", cmpnd_Tf_df$term, ignore.case=TRUE, value=TRUE)
print(sprintf("nrow(cmpnd_Tf_df): %d", nrow(cmpnd_Tf_df)))
myprint_df(cmpnd_Tf_df)
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text_reporting_compound_terms"), major.inc=FALSE)
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining compound terms in %s: ", txt_var))
txt_vctr <- glb_txt_lst[[txt_var]]
# find_cmpnd_wrds(txt_vctr)
#grep("thirty-five", txt_vctr, ignore.case=TRUE, value=TRUE)
#rex_str <- glb_txt_map_df[grepl("hirty", glb_txt_map_df$rex_str), "rex_str"]
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "build.corpus"), major.inc=TRUE)
glb_corpus_lst <- list()
print(sprintf("Building glb_corpus_lst..."))
glb_corpus_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_corpus <- Corpus(VectorSource(glb_txt_lst[[txt_var]]))
txt_corpus <- tm_map(txt_corpus, tolower) #nuppr
txt_corpus <- tm_map(txt_corpus, PlainTextDocument)
txt_corpus <- tm_map(txt_corpus, removePunctuation) #npnct<chr_ix>
# txt-corpus <- tm_map(txt_corpus, content_transformer(function(x, pattern) gsub(pattern, "", x))
# Not to be run in production
inspect_terms <- function() {
full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTf))
print(" Full TermMatrix:"); print(full_Tf_DTM)
full_Tf_mtrx <- as.matrix(full_Tf_DTM)
rownames(full_Tf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_Tf_vctr <- colSums(full_Tf_mtrx)
names(full_Tf_vctr) <- dimnames(full_Tf_DTM)[[2]]
#grep("year", names(full_Tf_vctr), value=TRUE)
#which.max(full_Tf_mtrx[, "yearlong"])
full_Tf_df <- as.data.frame(full_Tf_vctr)
names(full_Tf_df) <- "Tf.full"
full_Tf_df$term <- rownames(full_Tf_df)
#full_Tf_df$freq.full <- colSums(full_Tf_mtrx != 0)
full_Tf_df <- orderBy(~ -Tf.full +term, full_Tf_df)
print(myplot_histogram(full_Tf_df, "Tf.full"))
myprint_df(full_Tf_df)
#txt_corpus[[which(grepl("zun", txt_vctr, ignore.case=TRUE))]]
digit_terms_df <- subset(full_Tf_df, grepl("[[:digit:]]", term))
myprint_df(digit_terms_df)
return(full_Tf_df)
}
#print("RemovePunct:"); remove_punct_Tf_df <- inspect_terms()
txt_corpus <- tm_map(txt_corpus, removeWords,
c(glb_append_stop_words[[txt_var]],
stopwords("english"))) #nstopwrds
#print("StoppedWords:"); stopped_words_Tf_df <- inspect_terms()
txt_corpus <- tm_map(txt_corpus, stemDocument) #Features for lost information: Difference/ratio in density of full_TfIdf_DTM ???
#txt_corpus <- tm_map(txt_corpus, content_transformer(stemDocument))
#print("StemmedWords:"); stemmed_words_Tf_df <- inspect_terms()
#stemmed_stopped_Tf_df <- merge(stemmed_words_Tf_df, stopped_words_Tf_df, by="term", all=TRUE, suffixes=c(".stem", ".stop"))
#myprint_df(stemmed_stopped_Tf_df)
#print(subset(stemmed_stopped_Tf_df, grepl("compan", term)))
#glb_corpus_lst[[txt_var]] <- txt_corpus
}
names(glb_corpus_lst) <- glb_txt_vars
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "extract.DTM"), major.inc=TRUE)
glb_full_DTM_lst <- list(); glb_sprs_DTM_lst <- list();
for (txt_var in glb_txt_vars) {
print(sprintf("Extracting TfIDf terms for %s...", txt_var))
txt_corpus <- glb_corpus_lst[[txt_var]]
# full_Tf_DTM <- DocumentTermMatrix(txt_corpus,
# control=list(weighting=weightTf))
full_TfIdf_DTM <- DocumentTermMatrix(txt_corpus,
control=list(weighting=weightTfIdf))
sprs_TfIdf_DTM <- removeSparseTerms(full_TfIdf_DTM,
glb_sprs_thresholds[txt_var])
# glb_full_DTM_lst[[txt_var]] <- full_Tf_DTM
# glb_sprs_DTM_lst[[txt_var]] <- sprs_Tf_DTM
glb_full_DTM_lst[[txt_var]] <- full_TfIdf_DTM
glb_sprs_DTM_lst[[txt_var]] <- sprs_TfIdf_DTM
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "report.DTM"), major.inc=TRUE)
for (txt_var in glb_txt_vars) {
print(sprintf("Reporting TfIDf terms for %s...", txt_var))
full_TfIdf_DTM <- glb_full_DTM_lst[[txt_var]]
sprs_TfIdf_DTM <- glb_sprs_DTM_lst[[txt_var]]
print(" Full TermMatrix:"); print(full_TfIdf_DTM)
full_TfIdf_mtrx <- as.matrix(full_TfIdf_DTM)
rownames(full_TfIdf_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
full_TfIdf_vctr <- colSums(full_TfIdf_mtrx)
names(full_TfIdf_vctr) <- dimnames(full_TfIdf_DTM)[[2]]
#grep("scene", names(full_TfIdf_vctr), value=TRUE)
#which.max(full_TfIdf_mtrx[, "yearlong"])
full_TfIdf_df <- as.data.frame(full_TfIdf_vctr)
names(full_TfIdf_df) <- "TfIdf.full"
full_TfIdf_df$term <- rownames(full_TfIdf_df)
full_TfIdf_df$freq.full <- colSums(full_TfIdf_mtrx != 0)
full_TfIdf_df <- orderBy(~ -TfIdf.full, full_TfIdf_df)
print(" Sparse TermMatrix:"); print(sprs_TfIdf_DTM)
sprs_TfIdf_vctr <- colSums(as.matrix(sprs_TfIdf_DTM))
names(sprs_TfIdf_vctr) <- dimnames(sprs_TfIdf_DTM)[[2]]
sprs_TfIdf_df <- as.data.frame(sprs_TfIdf_vctr)
names(sprs_TfIdf_df) <- "TfIdf.sprs"
sprs_TfIdf_df$term <- rownames(sprs_TfIdf_df)
sprs_TfIdf_df$freq.sprs <- colSums(as.matrix(sprs_TfIdf_DTM) != 0)
sprs_TfIdf_df <- orderBy(~ -TfIdf.sprs, sprs_TfIdf_df)
terms_TfIdf_df <- merge(full_TfIdf_df, sprs_TfIdf_df, all.x=TRUE)
terms_TfIdf_df$in.sprs <- !is.na(terms_TfIdf_df$freq.sprs)
plt_TfIdf_df <- subset(terms_TfIdf_df,
TfIdf.full >= min(terms_TfIdf_df$TfIdf.sprs, na.rm=TRUE))
plt_TfIdf_df$label <- ""
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "label"] <-
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"]
glb_important_terms[[txt_var]] <- union(glb_important_terms[[txt_var]],
plt_TfIdf_df[is.na(plt_TfIdf_df$TfIdf.sprs), "term"])
print(myplot_scatter(plt_TfIdf_df, "freq.full", "TfIdf.full",
colorcol_name="in.sprs") +
geom_text(aes(label=label), color="Black", size=3.5))
melt_TfIdf_df <- orderBy(~ -value, melt(terms_TfIdf_df, id.var="term"))
print(ggplot(melt_TfIdf_df, aes(value, color=variable)) + stat_ecdf() +
geom_hline(yintercept=glb_sprs_thresholds[txt_var],
linetype = "dotted"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, !is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(melt_TfIdf_df, "term", "value",
colorcol_name="variable"))
melt_TfIdf_df <- orderBy(~ -value,
melt(subset(terms_TfIdf_df, is.na(TfIdf.sprs)), id.var="term"))
print(myplot_hbar(head(melt_TfIdf_df, 10), "term", "value",
colorcol_name="variable"))
}
# sav_full_DTM_lst <- glb_full_DTM_lst
# sav_sprs_DTM_lst <- glb_sprs_DTM_lst
# print(identical(sav_glb_corpus_lst, glb_corpus_lst))
# print(all.equal(length(sav_glb_corpus_lst), length(glb_corpus_lst)))
# print(all.equal(names(sav_glb_corpus_lst), names(glb_corpus_lst)))
# print(all.equal(sav_glb_corpus_lst[["Headline"]], glb_corpus_lst[["Headline"]]))
# print(identical(sav_full_DTM_lst, glb_full_DTM_lst))
# print(identical(sav_sprs_DTM_lst, glb_sprs_DTM_lst))
rm(full_TfIdf_mtrx, full_TfIdf_df, melt_TfIdf_df, terms_TfIdf_df)
# Create txt features
if ((length(glb_txt_vars) > 1) &&
(length(unique(pfxs <- sapply(glb_txt_vars,
function(txt) toupper(substr(txt, 1, 1))))) < length(glb_txt_vars)))
stop("Prefixes for corpus freq terms not unique: ", pfxs)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DTM"),
major.inc=TRUE)
for (txt_var in glb_txt_vars) {
print(sprintf("Binding DTM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_X_df <- as.data.frame(as.matrix(glb_sprs_DTM_lst[[txt_var]]))
colnames(txt_X_df) <- paste(txt_var_pfx, ".T.",
make.names(colnames(txt_X_df)), sep="")
rownames(txt_X_df) <- rownames(glb_allobs_df) # warning otherwise
# plt_X_df <- cbind(txt_X_df, glb_allobs_df[, c(glb_id_vars, glb_rsp_var)])
# print(myplot_box(df=plt_X_df, ycol_names="H.T.today", xcol_name=glb_rsp_var))
# log_X_df <- log(1 + txt_X_df)
# colnames(log_X_df) <- paste(colnames(txt_X_df), ".log", sep="")
# plt_X_df <- cbind(log_X_df, glb_allobs_df[, c(glb_id_vars, glb_rsp_var)])
# print(myplot_box(df=plt_X_df, ycol_names="H.T.today.log", xcol_name=glb_rsp_var))
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df) # TfIdf is normalized
#glb_allobs_df <- cbind(glb_allobs_df, log_X_df) # if using non-normalized metrics
}
#identical(chk_entity_df, glb_allobs_df)
#chk_entity_df <- glb_allobs_df
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DXM"),
major.inc=TRUE)
#sav_allobs_df <- glb_allobs_df
glb_punct_vctr <- c("!", "\"", "#", "\\$", "%", "&", "'",
"\\(|\\)",# "\\(", "\\)",
"\\*", "\\+", ",", "-", "\\.", "/", ":", ";",
"<|>", # "<",
"=",
# ">",
"\\?", "@", "\\[", "\\\\", "\\]", "^", "_", "`",
"\\{", "\\|", "\\}", "~")
txt_X_df <- glb_allobs_df[, c(glb_id_vars, ".rnorm"), FALSE]
txt_X_df <- foreach(txt_var=glb_txt_vars, .combine=cbind) %dopar% {
#for (txt_var in glb_txt_vars) {
print(sprintf("Binding DXM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
#txt_X_df <- glb_allobs_df[, c(glb_id_vars, ".rnorm"), FALSE]
txt_full_DTM_mtrx <- as.matrix(glb_full_DTM_lst[[txt_var]])
rownames(txt_full_DTM_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
#print(txt_full_DTM_mtrx[txt_full_DTM_mtrx[, "ebola"] != 0, "ebola"])
# Create <txt_var>.T.<term> for glb_important_terms
for (term in glb_important_terms[[txt_var]])
txt_X_df[, paste0(txt_var_pfx, ".T.", make.names(term))] <-
txt_full_DTM_mtrx[, term]
# Create <txt_var>.nwrds.log & .nwrds.unq.log
txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")] <-
log(1 + mycount_pattern_occ("\\w+", glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".nwrds.unq.log")] <-
log(1 + rowSums(txt_full_DTM_mtrx != 0))
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] <-
rowSums(txt_full_DTM_mtrx)
txt_X_df[, paste0(txt_var_pfx, ".ratio.sum.TfIdf.nwrds")] <-
txt_X_df[, paste0(txt_var_pfx, ".sum.TfIdf")] /
(exp(txt_X_df[, paste0(txt_var_pfx, ".nwrds.log")]) - 1)
# Create <txt_var>.nchrs.log
txt_X_df[, paste0(txt_var_pfx, ".nchrs.log")] <-
log(1 + mycount_pattern_occ(".", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".nuppr.log")] <-
log(1 + mycount_pattern_occ("[[:upper:]]", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".ndgts.log")] <-
log(1 + mycount_pattern_occ("[[:digit:]]", glb_allobs_df[, txt_var]))
# Create <txt_var>.npnct?.log
# would this be faster if it's iterated over each row instead of
# each created column ???
for (punct_ix in 1:length(glb_punct_vctr)) {
# smp0 <- " "
# smp1 <- "! \" # $ % & ' ( ) * + , - . / : ; < = > ? @ [ \ ] ^ _ ` { | } ~"
# smp2 <- paste(smp1, smp1, sep=" ")
# print(sprintf("Testing %s pattern:", glb_punct_vctr[punct_ix]))
# results <- mycount_pattern_occ(glb_punct_vctr[punct_ix], c(smp0, smp1, smp2))
# names(results) <- NULL; print(results)
txt_X_df[,
paste0(txt_var_pfx, ".npnct", sprintf("%02d", punct_ix), ".log")] <-
log(1 + mycount_pattern_occ(glb_punct_vctr[punct_ix],
glb_allobs_df[, txt_var]))
}
# print(head(glb_allobs_df[glb_allobs_df[, "A.npnct23.log"] > 0,
# c("UniqueID", "Popular", "Abstract", "A.npnct23.log")]))
# Create <txt_var>.nstopwrds.log & <txt_var>ratio.nstopwrds.nwrds
stop_words_rex_str <- paste0("\\b(", paste0(c(glb_append_stop_words[[txt_var]],
stopwords("english")), collapse="|"),
")\\b")
txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] <-
log(1 + mycount_pattern_occ(stop_words_rex_str, glb_txt_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".ratio.nstopwrds.nwrds")] <-
exp(txt_X_df[, paste0(txt_var_pfx, ".nstopwrds", ".log")] -
txt_X_df[, paste0(txt_var_pfx, ".nwrds", ".log")])
# Create <txt_var>.P.http
txt_X_df[, paste(txt_var_pfx, ".P.http", sep="")] <-
as.integer(0 + mycount_pattern_occ("http", glb_allobs_df[, txt_var]))
# Create user-specified pattern vectors
# <txt_var>.P.year.colon
txt_X_df[, paste0(txt_var_pfx, ".P.year.colon")] <-
as.integer(0 + mycount_pattern_occ("[0-9]{4}:", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.daily.clip.report")] <-
as.integer(0 + mycount_pattern_occ("Daily Clip Report", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.fashion.week")] <-
as.integer(0 + mycount_pattern_occ("Fashion Week", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.first.draft")] <-
as.integer(0 + mycount_pattern_occ("First Draft", glb_allobs_df[, txt_var]))
#sum(mycount_pattern_occ("Metropolitan Diary:", glb_allobs_df$Abstract) > 0)
if (txt_var %in% c("Snippet", "Abstract")) {
txt_X_df[, paste0(txt_var_pfx, ".P.metropolitan.diary.colon")] <-
as.integer(0 + mycount_pattern_occ("Metropolitan Diary:",
glb_allobs_df[, txt_var]))
}
#sum(mycount_pattern_occ("[0-9]{4}:", glb_allobs_df$Headline) > 0)
#sum(mycount_pattern_occ("Quandary(.*)(?=:)", glb_allobs_df$Headline, perl=TRUE) > 0)
#sum(mycount_pattern_occ("No Comment(.*):", glb_allobs_df$Headline) > 0)
#sum(mycount_pattern_occ("Friday Night Music:", glb_allobs_df$Headline) > 0)
if (txt_var %in% c("Headline")) {
txt_X_df[, paste0(txt_var_pfx, ".P.facts.figures")] <-
as.integer(0 + mycount_pattern_occ("Facts & Figures:", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.friday.night.music")] <-
as.integer(0 + mycount_pattern_occ("Friday Night Music", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.no.comment.colon")] <-
as.integer(0 + mycount_pattern_occ("No Comment(.*):", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.on.this.day")] <-
as.integer(0 + mycount_pattern_occ("On This Day", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.quandary")] <-
as.integer(0 + mycount_pattern_occ("Quandary(.*)(?=:)", glb_allobs_df[, txt_var], perl=TRUE))
txt_X_df[, paste0(txt_var_pfx, ".P.readers.respond")] <-
as.integer(0 + mycount_pattern_occ("Readers Respond", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.recap.colon")] <-
as.integer(0 + mycount_pattern_occ("Recap:", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.s.notebook")] <-
as.integer(0 + mycount_pattern_occ("s Notebook", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.today.in.politic")] <-
as.integer(0 + mycount_pattern_occ("Today in Politic", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.today.in.smallbusiness")] <-
as.integer(0 + mycount_pattern_occ("Today in Small Business:", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.verbatim.colon")] <-
as.integer(0 + mycount_pattern_occ("Verbatim:", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".P.what.we.are")] <-
as.integer(0 + mycount_pattern_occ("What We're", glb_allobs_df[, txt_var]))
}
#summary(glb_allobs_df[ ,grep("P.on.this.day", names(glb_allobs_df), value=TRUE)])
txt_X_df <- subset(txt_X_df, select=-.rnorm)
txt_X_df <- txt_X_df[, -grep(glb_id_vars, names(txt_X_df), fixed=TRUE), FALSE]
#glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
}
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
#myplot_box(glb_allobs_df, "A.sum.TfIdf", glb_rsp_var)
# Generate summaries
# print(summary(glb_allobs_df))
# print(sapply(names(glb_allobs_df), function(col) sum(is.na(glb_allobs_df[, col]))))
# print(summary(glb_trnobs_df))
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(summary(glb_newobs_df))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
rm(log_X_df, txt_X_df)
}
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# print(myplot_scatter(glb_trnobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
glb_full_DTM_lst, glb_sprs_DTM_lst, txt_corpus, txt_vctr)## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'corpus_lst' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'full_TfIdf_DTM' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'full_TfIdf_vctr' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'glb_full_DTM_lst' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'glb_sprs_DTM_lst' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'txt_corpus' not found## Warning in rm(corpus_lst, full_TfIdf_DTM, full_TfIdf_vctr,
## glb_full_DTM_lst, : object 'txt_vctr' not foundextract.features_chunk_df <- myadd_chunk(extract.features_chunk_df, "extract.features_end",
major.inc=TRUE)## label step_major step_minor bgn end
## 2 extract.features_factorize.str.vars 2 0 26.326 26.342
## 3 extract.features_end 3 0 26.342 NA
## elapsed
## 2 0.016
## 3 NAmyplt_chunk(extract.features_chunk_df)## label step_major step_minor bgn end
## 2 extract.features_factorize.str.vars 2 0 26.326 26.342
## 1 extract.features_bgn 1 0 26.318 26.325
## elapsed duration
## 2 0.016 0.016
## 1 0.007 0.007
## [1] "Total Elapsed Time: 26.342 secs"
# if (glb_save_envir)
# save(glb_feats_df,
# glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
# file=paste0(glb_out_pfx, "extract_features_dsk.RData"))
# load(paste0(glb_out_pfx, "extract_features_dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all","data.new")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "cluster.data", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 6 extract.features 3 0 26.277 27.379 1.102
## 7 cluster.data 4 0 27.379 NA NAStep 4.0: cluster data
if (glb_cluster) {
require(proxy)
#require(hash)
require(dynamicTreeCut)
# glb_hash <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
# glb_hash_lst <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
#stophere; sav_allobs_df <- glb_allobs_df;
print("Clustering features: ")
print(cluster_vars <- grep("[HSA]\\.[PT]\\.", names(glb_allobs_df), value=TRUE))
#print(cluster_vars <- grep("[HSA]\\.", names(glb_allobs_df), value=TRUE))
glb_allobs_df$.clusterid <- 1
#print(max(table(glb_allobs_df$myCategory.fctr) / 20))
for (myCategory in c("##", "Business#Business Day#Dealbook", "OpEd#Opinion#",
"Styles#U.S.#", "Business#Technology#", "Science#Health#",
"Culture#Arts#")) {
ctgry_allobs_df <- glb_allobs_df[glb_allobs_df$myCategory == myCategory, ]
dstns_dist <- dist(ctgry_allobs_df[, cluster_vars], method = "cosine")
dstns_mtrx <- as.matrix(dstns_dist)
print(sprintf("max distance(%0.4f) pair:", max(dstns_mtrx)))
row_ix <- ceiling(which.max(dstns_mtrx) / ncol(dstns_mtrx))
col_ix <- which.max(dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c("UniqueID", "Popular", "myCategory", "Headline", cluster_vars)])
min_dstns_mtrx <- dstns_mtrx
diag(min_dstns_mtrx) <- 1
print(sprintf("min distance(%0.4f) pair:", min(min_dstns_mtrx)))
row_ix <- ceiling(which.min(min_dstns_mtrx) / ncol(min_dstns_mtrx))
col_ix <- which.min(min_dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c("UniqueID", "Popular", "myCategory", "Headline", cluster_vars)])
clusters <- hclust(dstns_dist, method = "ward.D2")
#plot(clusters, labels=NULL, hang=-1)
myplclust(clusters, lab.col=unclass(ctgry_allobs_df[, glb_rsp_var]))
#clusterGroups = cutree(clusters, k=7)
clusterGroups <- cutreeDynamic(clusters, minClusterSize=20, method="tree", deepSplit=0)
# Unassigned groups are labeled 0; the largest group has label 1
table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
#print(ctgry_allobs_df[which(clusterGroups == 1), c("UniqueID", "Popular", "Headline")])
#print(ctgry_allobs_df[(clusterGroups == 1) & !is.na(ctgry_allobs_df$Popular) & (ctgry_allobs_df$Popular==1), c("UniqueID", "Popular", "Headline")])
clusterGroups[clusterGroups == 0] <- 1
table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
#summary(factor(clusterGroups))
# clusterGroups <- clusterGroups +
# 100 * # has to be > max(table(glb_allobs_df$myCategory.fctr) / minClusterSize=20)
# which(levels(glb_allobs_df$myCategory.fctr) == myCategory)
# table(clusterGroups, ctgry_allobs_df[, glb_rsp_var], useNA="ifany")
# add to glb_allobs_df - then split the data again
glb_allobs_df[glb_allobs_df$myCategory==myCategory,]$.clusterid <- clusterGroups
#print(unique(glb_allobs_df$.clusterid))
#print(glb_feats_df[glb_feats_df$id == ".clusterid.fctr", ])
}
ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
mycreate_sqlxtab_df(glb_allobs_df,
c("myCategory", ".clusterid", glb_rsp_var)))
ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
myCategory + .clusterid ~
Popular.fctr, sum, value.var=".n"))
print(ctgry_cast_df)
#print(orderBy(~ myCategory -Y -NA, ctgry_cast_df))
# write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_clst.csv"),
# row.names=FALSE)
print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df$.clusterid,
glb_allobs_df[, glb_rsp_var],
useNA="ifany"))
# dsp_obs(.clusterid=1, myCategory="OpEd#Opinion#",
# cols=c("UniqueID", "Popular", "myCategory", ".clusterid", "Headline"),
# all=TRUE)
glb_allobs_df$.clusterid.fctr <- as.factor(glb_allobs_df$.clusterid)
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
".clusterid")
glb_interaction_only_features["myCategory.fctr"] <- c(".clusterid.fctr")
}
# Re-partition
glb_trnobs_df <- subset(glb_allobs_df, .src == "Train")
glb_newobs_df <- subset(glb_allobs_df, .src == "Test")
glb_chunks_df <- myadd_chunk(glb_chunks_df, "select.features", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 7 cluster.data 4 0 27.379 27.658 0.279
## 8 select.features 5 0 27.658 NA NAStep 5.0: select features
print(glb_feats_df <- myselect_features(entity_df=glb_trnobs_df,
exclude_vars_as_features=glb_exclude_vars_as_features,
rsp_var=glb_rsp_var))## id
## ChildMortality ChildMortality
## Under15 Under15
## FertilityRate FertilityRate
## FertilityRate.nonNA FertilityRate.nonNA
## LiteracyRate LiteracyRate
## PrimarySchoolEnrollmentFemale PrimarySchoolEnrollmentFemale
## Over60 Over60
## GNI GNI
## LiteracyRate.nonNA LiteracyRate.nonNA
## PrimarySchoolEnrollmentFemale.nonNA PrimarySchoolEnrollmentFemale.nonNA
## GNI.nonNA GNI.nonNA
## CellularSubscribers CellularSubscribers
## PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentMale
## CellularSubscribers.nonNA CellularSubscribers.nonNA
## PrimarySchoolEnrollmentMale.nonNA PrimarySchoolEnrollmentMale.nonNA
## Region.fctr Region.fctr
## .rnorm .rnorm
## Population Population
## cor.y exclude.as.feat
## ChildMortality -0.930540154 0
## Under15 -0.828267850 0
## FertilityRate -0.821220176 1
## FertilityRate.nonNA -0.812058404 0
## LiteracyRate 0.724901262 1
## PrimarySchoolEnrollmentFemale 0.715035749 1
## Over60 0.693014776 0
## GNI 0.673926784 1
## LiteracyRate.nonNA 0.665663573 0
## PrimarySchoolEnrollmentFemale.nonNA 0.650665778 0
## GNI.nonNA 0.631623396 0
## CellularSubscribers 0.628898883 1
## PrimarySchoolEnrollmentMale 0.623339427 1
## CellularSubscribers.nonNA 0.621594090 0
## PrimarySchoolEnrollmentMale.nonNA 0.519297226 0
## Region.fctr -0.453295147 0
## .rnorm -0.033249865 0
## Population 0.007149689 0
## cor.y.abs
## ChildMortality 0.930540154
## Under15 0.828267850
## FertilityRate 0.821220176
## FertilityRate.nonNA 0.812058404
## LiteracyRate 0.724901262
## PrimarySchoolEnrollmentFemale 0.715035749
## Over60 0.693014776
## GNI 0.673926784
## LiteracyRate.nonNA 0.665663573
## PrimarySchoolEnrollmentFemale.nonNA 0.650665778
## GNI.nonNA 0.631623396
## CellularSubscribers 0.628898883
## PrimarySchoolEnrollmentMale 0.623339427
## CellularSubscribers.nonNA 0.621594090
## PrimarySchoolEnrollmentMale.nonNA 0.519297226
## Region.fctr 0.453295147
## .rnorm 0.033249865
## Population 0.007149689# sav_feats_df <- glb_feats_df; glb_feats_df <- sav_feats_df
print(glb_feats_df <- orderBy(~-cor.y,
myfind_cor_features(feats_df=glb_feats_df, obs_df=glb_trnobs_df,
rsp_var=glb_rsp_var)))## Loading required package: reshape2## [1] "cor(FertilityRate.nonNA, Under15)=0.9261"
## [1] "cor(LifeExpectancy, FertilityRate.nonNA)=-0.8121"
## [1] "cor(LifeExpectancy, Under15)=-0.8283"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified FertilityRate.nonNA as highly correlated with
## Under15## [1] "cor(PrimarySchoolEnrollmentFemale.nonNA, PrimarySchoolEnrollmentMale.nonNA)=0.9117"
## [1] "cor(LifeExpectancy, PrimarySchoolEnrollmentFemale.nonNA)=0.6507"
## [1] "cor(LifeExpectancy, PrimarySchoolEnrollmentMale.nonNA)=0.5193"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified PrimarySchoolEnrollmentMale.nonNA as highly
## correlated with PrimarySchoolEnrollmentFemale.nonNA## [1] "cor(Over60, Under15)=-0.8250"
## [1] "cor(LifeExpectancy, Over60)=0.6930"
## [1] "cor(LifeExpectancy, Under15)=-0.8283"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified Over60 as highly correlated with Under15## [1] "cor(ChildMortality, Under15)=0.8084"
## [1] "cor(LifeExpectancy, ChildMortality)=-0.9305"
## [1] "cor(LifeExpectancy, Under15)=-0.8283"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df
## = glb_trnobs_df, : Identified Under15 as highly correlated with
## ChildMortality## [1] "cor(ChildMortality, LiteracyRate.nonNA)=-0.7532"
## [1] "cor(LifeExpectancy, ChildMortality)=-0.9305"
## [1] "cor(LifeExpectancy, LiteracyRate.nonNA)=0.6657"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified LiteracyRate.nonNA as highly correlated with
## ChildMortality## [1] "cor(ChildMortality, PrimarySchoolEnrollmentFemale.nonNA)=-0.7005"
## [1] "cor(LifeExpectancy, ChildMortality)=-0.9305"
## [1] "cor(LifeExpectancy, PrimarySchoolEnrollmentFemale.nonNA)=0.6507"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified PrimarySchoolEnrollmentFemale.nonNA as highly
## correlated with ChildMortality## id
## LiteracyRate LiteracyRate
## PrimarySchoolEnrollmentFemale PrimarySchoolEnrollmentFemale
## Over60 Over60
## GNI GNI
## LiteracyRate.nonNA LiteracyRate.nonNA
## PrimarySchoolEnrollmentFemale.nonNA PrimarySchoolEnrollmentFemale.nonNA
## GNI.nonNA GNI.nonNA
## CellularSubscribers CellularSubscribers
## PrimarySchoolEnrollmentMale PrimarySchoolEnrollmentMale
## CellularSubscribers.nonNA CellularSubscribers.nonNA
## PrimarySchoolEnrollmentMale.nonNA PrimarySchoolEnrollmentMale.nonNA
## Population Population
## .rnorm .rnorm
## Region.fctr Region.fctr
## FertilityRate.nonNA FertilityRate.nonNA
## FertilityRate FertilityRate
## Under15 Under15
## ChildMortality ChildMortality
## cor.y exclude.as.feat
## LiteracyRate 0.724901262 1
## PrimarySchoolEnrollmentFemale 0.715035749 1
## Over60 0.693014776 0
## GNI 0.673926784 1
## LiteracyRate.nonNA 0.665663573 0
## PrimarySchoolEnrollmentFemale.nonNA 0.650665778 0
## GNI.nonNA 0.631623396 0
## CellularSubscribers 0.628898883 1
## PrimarySchoolEnrollmentMale 0.623339427 1
## CellularSubscribers.nonNA 0.621594090 0
## PrimarySchoolEnrollmentMale.nonNA 0.519297226 0
## Population 0.007149689 0
## .rnorm -0.033249865 0
## Region.fctr -0.453295147 0
## FertilityRate.nonNA -0.812058404 0
## FertilityRate -0.821220176 1
## Under15 -0.828267850 0
## ChildMortality -0.930540154 0
## cor.y.abs
## LiteracyRate 0.724901262
## PrimarySchoolEnrollmentFemale 0.715035749
## Over60 0.693014776
## GNI 0.673926784
## LiteracyRate.nonNA 0.665663573
## PrimarySchoolEnrollmentFemale.nonNA 0.650665778
## GNI.nonNA 0.631623396
## CellularSubscribers 0.628898883
## PrimarySchoolEnrollmentMale 0.623339427
## CellularSubscribers.nonNA 0.621594090
## PrimarySchoolEnrollmentMale.nonNA 0.519297226
## Population 0.007149689
## .rnorm 0.033249865
## Region.fctr 0.453295147
## FertilityRate.nonNA 0.812058404
## FertilityRate 0.821220176
## Under15 0.828267850
## ChildMortality 0.930540154
## cor.high.X
## LiteracyRate <NA>
## PrimarySchoolEnrollmentFemale <NA>
## Over60 Under15
## GNI <NA>
## LiteracyRate.nonNA ChildMortality
## PrimarySchoolEnrollmentFemale.nonNA ChildMortality
## GNI.nonNA <NA>
## CellularSubscribers <NA>
## PrimarySchoolEnrollmentMale <NA>
## CellularSubscribers.nonNA <NA>
## PrimarySchoolEnrollmentMale.nonNA PrimarySchoolEnrollmentFemale.nonNA
## Population <NA>
## .rnorm <NA>
## Region.fctr <NA>
## FertilityRate.nonNA Under15
## FertilityRate <NA>
## Under15 ChildMortality
## ChildMortality <NA>
## freqRatio percentUnique zeroVar nzv
## LiteracyRate 1.000000 44.604317 FALSE FALSE
## PrimarySchoolEnrollmentFemale 1.000000 39.568345 FALSE FALSE
## Over60 2.000000 93.525180 FALSE FALSE
## GNI 1.000000 79.856115 FALSE FALSE
## LiteracyRate.nonNA 1.000000 50.359712 FALSE FALSE
## PrimarySchoolEnrollmentFemale.nonNA 1.000000 46.762590 FALSE FALSE
## GNI.nonNA 1.500000 84.892086 FALSE FALSE
## CellularSubscribers 1.000000 93.525180 FALSE FALSE
## PrimarySchoolEnrollmentMale 1.000000 42.446043 FALSE FALSE
## CellularSubscribers.nonNA 1.000000 94.244604 FALSE FALSE
## PrimarySchoolEnrollmentMale.nonNA 1.000000 48.201439 FALSE FALSE
## Population 1.000000 98.561151 FALSE FALSE
## .rnorm 1.000000 100.000000 FALSE FALSE
## Region.fctr 1.181818 4.316547 FALSE FALSE
## FertilityRate.nonNA 1.333333 79.136691 FALSE FALSE
## FertilityRate 1.000000 78.417266 FALSE FALSE
## Under15 2.000000 94.244604 FALSE FALSE
## ChildMortality 1.500000 91.366906 FALSE FALSE
## myNearZV is.cor.y.abs.low
## LiteracyRate FALSE FALSE
## PrimarySchoolEnrollmentFemale FALSE FALSE
## Over60 FALSE FALSE
## GNI FALSE FALSE
## LiteracyRate.nonNA FALSE FALSE
## PrimarySchoolEnrollmentFemale.nonNA FALSE FALSE
## GNI.nonNA FALSE FALSE
## CellularSubscribers FALSE FALSE
## PrimarySchoolEnrollmentMale FALSE FALSE
## CellularSubscribers.nonNA FALSE FALSE
## PrimarySchoolEnrollmentMale.nonNA FALSE FALSE
## Population FALSE TRUE
## .rnorm FALSE FALSE
## Region.fctr FALSE FALSE
## FertilityRate.nonNA FALSE FALSE
## FertilityRate FALSE FALSE
## Under15 FALSE FALSE
## ChildMortality FALSE FALSE#subset(glb_feats_df, id %in% c("A.nuppr.log", "S.nuppr.log"))
print(myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
colorcol_name="myNearZV", jitter=TRUE) +
geom_point(aes(shape=nzv)) + xlim(-5, 25))## Warning in myplot_scatter(glb_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "myNearZV", : converting myNearZV to class:factor## Warning in loop_apply(n, do.ply): Removed 17 rows containing missing values
## (geom_point).## Warning in loop_apply(n, do.ply): Removed 17 rows containing missing values
## (geom_point).## Warning in loop_apply(n, do.ply): Removed 17 rows containing missing values
## (geom_point).
print(subset(glb_feats_df, myNearZV))## [1] id cor.y exclude.as.feat cor.y.abs
## [5] cor.high.X freqRatio percentUnique zeroVar
## [9] nzv myNearZV is.cor.y.abs.low
## <0 rows> (or 0-length row.names)glb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
subset(glb_feats_df, myNearZV)$id)]
if (!is.null(glb_interaction_only_features))
glb_feats_df[glb_feats_df$id %in% glb_interaction_only_features, "interaction.feat"] <-
names(glb_interaction_only_features) else
glb_feats_df$interaction.feat <- NA
glb_chunks_df <- myadd_chunk(glb_chunks_df, "partition.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 8 select.features 5 0 27.658 28.272 0.614
## 9 partition.data.training 6 0 28.272 NA NAStep 6.0: partition data training
if (all(is.na(glb_newobs_df[, glb_rsp_var]))) {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=1 - (nrow(glb_newobs_df) * 1.1 / nrow(glb_trnobs_df)))
glb_fitobs_df <- glb_trnobs_df[split, ]
glb_OOBobs_df <- glb_trnobs_df[!split ,]
} else {
print(sprintf("Newdata contains non-NA data for %s; setting OOB to Newdata",
glb_rsp_var))
glb_fitobs_df <- glb_trnobs_df; glb_OOBobs_df <- glb_newobs_df
}## [1] "Newdata contains non-NA data for LifeExpectancy; setting OOB to Newdata"if (!is.null(glb_max_fitent_obs) && (nrow(glb_fitobs_df) > glb_max_fitent_obs)) {
warning("glb_fitobs_df restricted to glb_max_fitent_obs: ",
format(glb_max_fitent_obs, big.mark=","))
org_fitent_df <- glb_fitobs_df
glb_fitobs_df <-
org_fitent_df[split <- sample.split(org_fitent_df[, glb_rsp_var_raw],
SplitRatio=glb_max_fitent_obs), ]
org_fitent_df <- NULL
}
glb_allobs_df$.lcn <- ""
glb_allobs_df[glb_allobs_df[, glb_id_vars] %in%
glb_fitobs_df[, glb_id_vars], ".lcn"] <- "Fit"
glb_allobs_df[glb_allobs_df[, glb_id_vars] %in%
glb_OOBobs_df[, glb_id_vars], ".lcn"] <- "OOB"
dsp_class_dstrb <- function(obs_df, location_var, partition_var) {
xtab_df <- mycreate_xtab_df(obs_df, c(location_var, partition_var))
rownames(xtab_df) <- xtab_df[, location_var]
xtab_df <- xtab_df[, -grepl(location_var, names(xtab_df))]
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Ensure proper splits by glb_rsp_var_raw & user-specified feature for OOB vs. new
if (!is.null(glb_category_vars)) {
dsp_class_dstrb(glb_allobs_df, ".lcn", glb_rsp_var_raw)
newent_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .src == "Test"),
"myCategory")
OOBobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .lcn == "OOB"),
"myCategory")
glb_ctgry_df <- merge(newent_ctgry_df, OOBobs_ctgry_df, by="myCategory", all=TRUE,
suffixes=c(".Tst", ".OOB"))
glb_ctgry_df$.freqRatio.Tst <- glb_ctgry_df$.n.Tst / sum(glb_ctgry_df$.n.Tst, na.rm=TRUE)
glb_ctgry_df$.freqRatio.OOB <- glb_ctgry_df$.n.OOB / sum(glb_ctgry_df$.n.OOB, na.rm=TRUE)
print(orderBy(~-.freqRatio.Tst-.freqRatio.OOB, glb_ctgry_df))
}
# Run this line by line
print("glb_feats_df:"); print(dim(glb_feats_df))## [1] "glb_feats_df:"## [1] 18 12sav_feats_df <- glb_feats_df
glb_feats_df <- sav_feats_df
glb_feats_df[, "rsp_var_raw"] <- FALSE
glb_feats_df[glb_feats_df$id == glb_rsp_var_raw, "rsp_var_raw"] <- TRUE
glb_feats_df$exclude.as.feat <- (glb_feats_df$exclude.as.feat == 1)
if (!is.null(glb_id_vars) && glb_id_vars != ".rownames")
glb_feats_df[glb_feats_df$id %in% glb_id_vars, "id_var"] <- TRUE
add_feats_df <- data.frame(id=glb_rsp_var, exclude.as.feat=TRUE, rsp_var=TRUE)
row.names(add_feats_df) <- add_feats_df$id; print(add_feats_df)## id exclude.as.feat rsp_var
## LifeExpectancy LifeExpectancy TRUE TRUEglb_feats_df <- myrbind_df(glb_feats_df, add_feats_df)
print(subset(glb_feats_df, rsp_var_raw | rsp_var | id_var))## id cor.y exclude.as.feat cor.y.abs cor.high.X
## LifeExpectancy LifeExpectancy NA TRUE NA <NA>
## freqRatio percentUnique zeroVar nzv myNearZV
## LifeExpectancy NA NA NA NA NA
## is.cor.y.abs.low interaction.feat rsp_var_raw id_var
## LifeExpectancy NA NA NA NA
## rsp_var
## LifeExpectancy TRUEprint("glb_feats_df vs. glb_allobs_df: "); ## [1] "glb_feats_df vs. glb_allobs_df: "print(setdiff(glb_feats_df$id, names(glb_allobs_df)))## character(0)print("glb_allobs_df vs. glb_feats_df: "); ## [1] "glb_allobs_df vs. glb_feats_df: "# Ensure these are only chr vars
print(setdiff(setdiff(names(glb_allobs_df), glb_feats_df$id),
myfind_chr_cols_df(glb_allobs_df)))## character(0)#print(setdiff(setdiff(names(glb_allobs_df), glb_exclude_vars_as_features),
# glb_feats_df$id))
print("glb_allobs_df: "); print(dim(glb_allobs_df))## [1] "glb_allobs_df: "## [1] 194 23print("glb_trnobs_df: "); print(dim(glb_trnobs_df))## [1] "glb_trnobs_df: "## [1] 139 22print("glb_fitobs_df: "); print(dim(glb_fitobs_df))## [1] "glb_fitobs_df: "## [1] 139 22print("glb_OOBobs_df: "); print(dim(glb_OOBobs_df))## [1] "glb_OOBobs_df: "## [1] 55 22print("glb_newobs_df: "); print(dim(glb_newobs_df))## [1] "glb_newobs_df: "## [1] 55 22# # Does not handle NULL or length(glb_id_vars) > 1
# glb_allobs_df$.src.trn <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_vars] %in% glb_trnobs_df[, glb_id_vars],
# ".src.trn"] <- 1
# glb_allobs_df$.src.fit <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_vars] %in% glb_fitobs_df[, glb_id_vars],
# ".src.fit"] <- 1
# glb_allobs_df$.src.OOB <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_vars] %in% glb_OOBobs_df[, glb_id_vars],
# ".src.OOB"] <- 1
# glb_allobs_df$.src.new <- 0
# glb_allobs_df[glb_allobs_df[, glb_id_vars] %in% glb_newobs_df[, glb_id_vars],
# ".src.new"] <- 1
# #print(unique(glb_allobs_df[, ".src.trn"]))
# write_cols <- c(glb_feats_df$id,
# ".src.trn", ".src.fit", ".src.OOB", ".src.new")
# glb_allobs_df <- glb_allobs_df[, write_cols]
#
# tmp_feats_df <- glb_feats_df
# tmp_entity_df <- glb_allobs_df
if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
file=paste0(glb_out_pfx, "blddfs_dsk.RData"))
# load(paste0(glb_out_pfx, "blddfs_dsk.RData"))
# if (!all.equal(tmp_feats_df, glb_feats_df))
# stop("glb_feats_df r/w not working")
# if (!all.equal(tmp_entity_df, glb_allobs_df))
# stop("glb_allobs_df r/w not working")
rm(split)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 9 partition.data.training 6 0 28.272 28.564 0.292
## 10 fit.models 7 0 28.564 NA NAStep 7.0: fit models
# load(paste0(glb_out_pfx, "dsk.RData"))
# keep_cols <- setdiff(names(glb_allobs_df),
# grep("^.src", names(glb_allobs_df), value=TRUE))
# glb_trnobs_df <- glb_allobs_df[glb_allobs_df$.src.trn == 1, keep_cols]
# glb_fitobs_df <- glb_allobs_df[glb_allobs_df$.src.fit == 1, keep_cols]
# glb_OOBobs_df <- glb_allobs_df[glb_allobs_df$.src.OOB == 1, keep_cols]
# glb_newobs_df <- glb_allobs_df[glb_allobs_df$.src.new == 1, keep_cols]
#
# glb_models_lst <- list(); glb_models_df <- data.frame()
#
if (glb_is_classification && glb_is_binomial &&
(length(unique(glb_fitobs_df[, glb_rsp_var])) < 2))
stop("glb_fitobs_df$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glb_fitobs_df[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
# while(length(max_cor_y_x_vars) < 2) {
# max_cor_y_x_vars <- c(max_cor_y_x_vars, orderBy(~ -cor.y.abs,
# subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low))[3, "id"])
# }
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a lower correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Baseline
if (!is.null(glb_Baseline_mdl_var))
ret_lst <- myfit_mdl_fn(model_id="Baseline", model_method="mybaseln_classfr",
indep_vars_vctr=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
ret_lst <- myfit_mdl(model_id="MFO",
model_method=ifelse(glb_is_regression, "lm", "myMFO_classfr"),
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: MFO.lm"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.571 -6.205 2.162 6.734 13.581
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 69.6506 0.8037 86.665 <2e-16 ***
## .rnorm -0.3269 0.8396 -0.389 0.698
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.443 on 137 degrees of freedom
## Multiple R-squared: 0.001106, Adjusted R-squared: -0.006186
## F-statistic: 0.1516 on 1 and 137 DF, p-value: 0.6976
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO.lm lm .rnorm 0 0.438
## min.elapsedtime.final max.R.sq.fit min.RMSE.fit max.R.sq.OOB
## 1 0.004 0.001105554 9.375044 0.009390806
## min.RMSE.OOB max.Adj.R.sq.fit
## 1 8.838414 -0.006185647if (glb_is_classification)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
ret_lst <- myfit_mdl(model_id="Random", model_method="myrandom_classfr",
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
# Any models that have tuning parameters has "better" results with cross-validation
# (except rf) & "different" results for different outcome metrics
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Max.cor.Y.cv.0.rpart"
## [1] " indep_vars: ChildMortality, GNI.nonNA"## Loading required package: rpart## Fitting cp = 0.698 on full training set## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 139
##
## CP nsplit rel error
## 1 0.6980808 0 1
##
## Node number 1: 139 observations
## mean=69.67626, MSE=87.98872
##
## n= 139
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 139 12230.43 69.67626 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats
## 1 Max.cor.Y.cv.0.rpart rpart ChildMortality, GNI.nonNA
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.511 0.009
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB
## 1 0 9.38023 0 8.880209ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0.cp.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=0,
tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))## [1] "fitting model: Max.cor.Y.cv.0.cp.0.rpart"
## [1] " indep_vars: ChildMortality, GNI.nonNA"
## Fitting cp = 0 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 139
##
## CP nsplit rel error
## 1 0.6980807729 0 1.00000000
## 2 0.1034320249 1 0.30191923
## 3 0.0796607700 2 0.19848720
## 4 0.0176912163 3 0.11882643
## 5 0.0173954340 4 0.10113522
## 6 0.0114193900 5 0.08373978
## 7 0.0079384852 6 0.07232039
## 8 0.0017231544 7 0.06438191
## 9 0.0009712485 8 0.06265875
## 10 0.0007929829 9 0.06168750
## 11 0.0007648582 10 0.06089452
## 12 0.0000000000 11 0.06012966
##
## Variable importance
## ChildMortality GNI.nonNA
## 61 39
##
## Node number 1: 139 observations, complexity param=0.6980808
## mean=69.67626, MSE=87.98872
## left son=2 (50 obs) right son=3 (89 obs)
## Primary splits:
## ChildMortality < 37.5 to the right, improve=0.6980808, (0 missing)
## GNI.nonNA < 3245 to the left, improve=0.4836100, (0 missing)
## Surrogate splits:
## GNI.nonNA < 3615 to the left, agree=0.899, adj=0.72, (0 split)
##
## Node number 2: 50 observations, complexity param=0.103432
## mean=59.22, MSE=38.4916
## left son=4 (22 obs) right son=5 (28 obs)
## Primary splits:
## ChildMortality < 78.65 to the right, improve=0.6572958, (0 missing)
## GNI.nonNA < 1075 to the left, improve=0.1047542, (0 missing)
## Surrogate splits:
## GNI.nonNA < 1075 to the left, agree=0.6, adj=0.091, (0 split)
##
## Node number 3: 89 observations, complexity param=0.07966077
## mean=75.55056, MSE=19.86542
## left son=6 (44 obs) right son=7 (45 obs)
## Primary splits:
## ChildMortality < 10.65 to the right, improve=0.5510595, (0 missing)
## GNI.nonNA < 30695 to the left, improve=0.4616534, (0 missing)
## Surrogate splits:
## GNI.nonNA < 14400 to the left, agree=0.82, adj=0.636, (0 split)
##
## Node number 4: 22 observations, complexity param=0.01141939
## mean=53.54545, MSE=13.15702
## left son=8 (7 obs) right son=9 (15 obs)
## Primary splits:
## ChildMortality < 118.6 to the right, improve=0.4825078, (0 missing)
## GNI.nonNA < 1090 to the left, improve=0.1211919, (0 missing)
## Surrogate splits:
## GNI.nonNA < 905 to the left, agree=0.773, adj=0.286, (0 split)
##
## Node number 5: 28 observations, complexity param=0.007938485
## mean=63.67857, MSE=13.21811
## left son=10 (11 obs) right son=11 (17 obs)
## Primary splits:
## GNI.nonNA < 1760 to the left, improve=0.2623324, (0 missing)
## ChildMortality < 61.45 to the right, improve=0.2582028, (0 missing)
## Surrogate splits:
## ChildMortality < 65.65 to the right, agree=0.714, adj=0.273, (0 split)
##
## Node number 6: 44 observations, complexity param=0.01769122
## mean=72.20455, MSE=9.617252
## left son=12 (11 obs) right son=13 (33 obs)
## Primary splits:
## ChildMortality < 25.5 to the right, improve=0.5113236, (0 missing)
## GNI.nonNA < 4825 to the left, improve=0.1726347, (0 missing)
## Surrogate splits:
## GNI.nonNA < 2800 to the left, agree=0.818, adj=0.273, (0 split)
##
## Node number 7: 45 observations, complexity param=0.01739543
## mean=78.82222, MSE=8.235062
## left son=14 (23 obs) right son=15 (22 obs)
## Primary splits:
## GNI.nonNA < 25905 to the left, improve=0.5741134, (0 missing)
## ChildMortality < 5.8 to the right, improve=0.5238786, (0 missing)
## Surrogate splits:
## ChildMortality < 5.4 to the right, agree=0.8, adj=0.591, (0 split)
##
## Node number 8: 7 observations
## mean=49.85714, MSE=3.55102
##
## Node number 9: 15 observations
## mean=55.26667, MSE=8.328889
##
## Node number 10: 11 observations
## mean=61.36364, MSE=11.86777
##
## Node number 11: 17 observations
## mean=65.17647, MSE=8.380623
##
## Node number 12: 11 observations
## mean=68.36364, MSE=6.049587
##
## Node number 13: 33 observations, complexity param=0.0009712485
## mean=73.48485, MSE=4.24977
## left son=26 (11 obs) right son=27 (22 obs)
## Primary splits:
## ChildMortality < 18.55 to the right, improve=0.08470182, (0 missing)
## GNI.nonNA < 5175 to the left, improve=0.05906979, (0 missing)
## Surrogate splits:
## GNI.nonNA < 4125 to the left, agree=0.697, adj=0.091, (0 split)
##
## Node number 14: 23 observations, complexity param=0.001723154
## mean=76.69565, MSE=4.646503
## left son=28 (16 obs) right son=29 (7 obs)
## Primary splits:
## ChildMortality < 6.05 to the right, improve=0.1972023, (0 missing)
## GNI.nonNA < 10140 to the right, improve=0.1270233, (0 missing)
## Surrogate splits:
## GNI.nonNA < 18330 to the left, agree=0.783, adj=0.286, (0 split)
##
## Node number 15: 22 observations, complexity param=0.0007648582
## mean=81.04545, MSE=2.316116
## left son=30 (15 obs) right son=31 (7 obs)
## Primary splits:
## GNI.nonNA < 50695 to the left, improve=0.1835861, (0 missing)
## ChildMortality < 3.5 to the right, improve=0.1224672, (0 missing)
## Surrogate splits:
## ChildMortality < 2.85 to the right, agree=0.727, adj=0.143, (0 split)
##
## Node number 26: 11 observations
## mean=72.63636, MSE=5.140496
##
## Node number 27: 22 observations, complexity param=0.0007929829
## mean=73.90909, MSE=3.264463
## left son=54 (9 obs) right son=55 (13 obs)
## Primary splits:
## GNI.nonNA < 7715 to the left, improve=0.1350427, (0 missing)
## ChildMortality < 13.55 to the left, improve=0.1181435, (0 missing)
## Surrogate splits:
## ChildMortality < 16.8 to the right, agree=0.773, adj=0.444, (0 split)
##
## Node number 28: 16 observations
## mean=76.0625, MSE=3.558594
##
## Node number 29: 7 observations
## mean=78.14286, MSE=4.122449
##
## Node number 30: 15 observations
## mean=80.6, MSE=2.506667
##
## Node number 31: 7 observations
## mean=82, MSE=0.5714286
##
## Node number 54: 9 observations
## mean=73.11111, MSE=4.54321
##
## Node number 55: 13 observations
## mean=74.46154, MSE=1.633136
##
## n= 139
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 139 12230.43000 69.67626
## 2) ChildMortality>=37.5 50 1924.58000 59.22000
## 4) ChildMortality>=78.65 22 289.45450 53.54545
## 8) ChildMortality>=118.6 7 24.85714 49.85714 *
## 9) ChildMortality< 118.6 15 124.93330 55.26667 *
## 5) ChildMortality< 78.65 28 370.10710 63.67857
## 10) GNI.nonNA< 1760 11 130.54550 61.36364 *
## 11) GNI.nonNA>=1760 17 142.47060 65.17647 *
## 3) ChildMortality< 37.5 89 1768.02200 75.55056
## 6) ChildMortality>=10.65 44 423.15910 72.20455
## 12) ChildMortality>=25.5 11 66.54545 68.36364 *
## 13) ChildMortality< 25.5 33 140.24240 73.48485
## 26) ChildMortality>=18.55 11 56.54545 72.63636 *
## 27) ChildMortality< 18.55 22 71.81818 73.90909
## 54) GNI.nonNA< 7715 9 40.88889 73.11111 *
## 55) GNI.nonNA>=7715 13 21.23077 74.46154 *
## 7) ChildMortality< 10.65 45 370.57780 78.82222
## 14) GNI.nonNA< 25905 23 106.86960 76.69565
## 28) ChildMortality>=6.05 16 56.93750 76.06250 *
## 29) ChildMortality< 6.05 7 28.85714 78.14286 *
## 15) GNI.nonNA>=25905 22 50.95455 81.04545
## 30) GNI.nonNA< 50695 15 37.60000 80.60000 *
## 31) GNI.nonNA>=50695 7 4.00000 82.00000 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats
## 1 Max.cor.Y.cv.0.cp.0.rpart rpart ChildMortality, GNI.nonNA
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.424 0.007
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.9398703 2.300159 0.8659683 3.251075if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.rpart"
## [1] " indep_vars: ChildMortality, GNI.nonNA"## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0797 on full training set## Warning in myfit_mdl(model_id = "Max.cor.Y", model_method = "rpart",
## model_type = glb_model_type, : model's bestTune found at an extreme of
## tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 139
##
## CP nsplit rel error
## 1 0.69808077 0 1.0000000
## 2 0.10343202 1 0.3019192
## 3 0.07966077 2 0.1984872
##
## Variable importance
## ChildMortality GNI.nonNA
## 61 39
##
## Node number 1: 139 observations, complexity param=0.6980808
## mean=69.67626, MSE=87.98872
## left son=2 (50 obs) right son=3 (89 obs)
## Primary splits:
## ChildMortality < 37.5 to the right, improve=0.6980808, (0 missing)
## GNI.nonNA < 3245 to the left, improve=0.4836100, (0 missing)
## Surrogate splits:
## GNI.nonNA < 3615 to the left, agree=0.899, adj=0.72, (0 split)
##
## Node number 2: 50 observations, complexity param=0.103432
## mean=59.22, MSE=38.4916
## left son=4 (22 obs) right son=5 (28 obs)
## Primary splits:
## ChildMortality < 78.65 to the right, improve=0.6572958, (0 missing)
## GNI.nonNA < 1075 to the left, improve=0.1047542, (0 missing)
## Surrogate splits:
## GNI.nonNA < 1075 to the left, agree=0.6, adj=0.091, (0 split)
##
## Node number 3: 89 observations
## mean=75.55056, MSE=19.86542
##
## Node number 4: 22 observations
## mean=53.54545, MSE=13.15702
##
## Node number 5: 28 observations
## mean=63.67857, MSE=13.21811
##
## n= 139
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 139 12230.4300 69.67626
## 2) ChildMortality>=37.5 50 1924.5800 59.22000
## 4) ChildMortality>=78.65 22 289.4545 53.54545 *
## 5) ChildMortality< 78.65 28 370.1071 63.67857 *
## 3) ChildMortality< 37.5 89 1768.0220 75.55056 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.rpart rpart ChildMortality, GNI.nonNA 3
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 1.127 0.009 0.8015128
## min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit min.RMSESD.fit
## 1 4.325582 0.764756 4.307076 0.8033837 0.6087925
## max.RsquaredSD.fit
## 1 0.07876564# Used to compare vs. Interactions.High.cor.Y and/or Max.cor.Y.TmSrs
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_vars,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.lm"
## [1] " indep_vars: ChildMortality, GNI.nonNA"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.2768 -1.4423 -0.0022 1.9361 7.7014
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 75.5206008 0.5134028 147.10 < 2e-16 ***
## ChildMortality -0.2000039 0.0076272 -26.22 < 2e-16 ***
## GNI.nonNA 0.0001175 0.0000170 6.91 1.71e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.988 on 136 degrees of freedom
## Multiple R-squared: 0.9008, Adjusted R-squared: 0.8993
## F-statistic: 617.1 on 2 and 136 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.lm lm ChildMortality, GNI.nonNA 1
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 0.817 0.003 0.900751
## min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit max.Rsquared.fit
## 1 3.003298 0.8554912 3.37575 0.8992914 0.9058445
## min.RMSESD.fit max.RsquaredSD.fit
## 1 0.7711282 0.05023664if (!is.null(glb_date_vars)) {
# ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly1",
# model_method=ifelse(glb_is_regression, "lm",
# ifelse(glb_is_binomial, "glm", "rpart")),
# model_type=glb_model_type,
# indep_vars_vctr=c(max_cor_y_x_vars, paste0(glb_date_vars, ".day.minutes")),
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
#
ret_lst <- myfit_mdl(model_id="Max.cor.Y.TmSrs.poly",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=c(max_cor_y_x_vars,
paste0(glb_date_vars, ".day.minutes.poly.", 1:5)),
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(unique(glb_feats_df$cor.high.X), NA)) > 0) {
# lm & glm handle interaction terms; rpart & rf do not
if (glb_is_regression || glb_is_binomial) {
indep_vars_vctr <-
c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":"))
} else { indep_vars_vctr <- union(max_cor_y_x_vars, int_feats) }
ret_lst <- myfit_mdl(model_id="Interact.High.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
} ## [1] "fitting model: Interact.High.cor.Y.lm"
## [1] " indep_vars: ChildMortality, GNI.nonNA, ChildMortality:Under15, ChildMortality:ChildMortality, ChildMortality:PrimarySchoolEnrollmentFemale.nonNA"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.4611 -1.3784 -0.0552 1.8585 8.5518
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 7.686e+01 6.400e-01
## ChildMortality -2.211e-01 6.066e-02
## GNI.nonNA 9.746e-05 1.731e-05
## `ChildMortality:Under15` 1.895e-03 1.047e-03
## `ChildMortality:PrimarySchoolEnrollmentFemale.nonNA` -9.681e-04 3.734e-04
## t value Pr(>|t|)
## (Intercept) 120.096 < 2e-16 ***
## ChildMortality -3.645 0.000382 ***
## GNI.nonNA 5.631 1.01e-07 ***
## `ChildMortality:Under15` 1.809 0.072723 .
## `ChildMortality:PrimarySchoolEnrollmentFemale.nonNA` -2.593 0.010572 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.872 on 134 degrees of freedom
## Multiple R-squared: 0.9097, Adjusted R-squared: 0.907
## F-statistic: 337.3 on 4 and 134 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 Interact.High.cor.Y.lm lm
## feats
## 1 ChildMortality, GNI.nonNA, ChildMortality:Under15, ChildMortality:ChildMortality, ChildMortality:PrimarySchoolEnrollmentFemale.nonNA
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 0.82 0.003
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.9096579 2.981268 0.8458567 3.486467 0.9069611
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.9116481 0.646475 0.03793371# Low.cor.X
# if (glb_is_classification && glb_is_binomial)
# indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) &
# is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"] else
indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) & !myNearZV &
(exclude.as.feat != 1))[, "id"]
myadjust_interaction_feats <- function(vars_vctr) {
for (feat in subset(glb_feats_df, !is.na(interaction.feat))$id)
if (feat %in% vars_vctr)
vars_vctr <- union(setdiff(vars_vctr, feat),
paste0(glb_feats_df[glb_feats_df$id == feat, "interaction.feat"], ":", feat))
return(vars_vctr)
}
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
ret_lst <- myfit_mdl(model_id="Low.cor.X",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Low.cor.X.lm"
## [1] " indep_vars: GNI.nonNA, CellularSubscribers.nonNA, Population, .rnorm, Region.fctr, ChildMortality"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.9613 -1.6261 0.1944 1.8748 6.1139
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.375e+01 1.341e+00 55.017 < 2e-16 ***
## GNI.nonNA 1.226e-04 1.785e-05 6.868 2.54e-10 ***
## CellularSubscribers.nonNA -1.523e-03 7.750e-03 -0.197 0.8445
## Population 1.007e-06 1.576e-06 0.639 0.5242
## .rnorm -5.259e-02 2.604e-01 -0.202 0.8403
## Region.fctrEurope 1.881e+00 9.845e-01 1.910 0.0583 .
## Region.fctrAmericas 2.514e+00 1.051e+00 2.392 0.0182 *
## `Region.fctrSouth-East Asia` 8.839e-01 1.275e+00 0.693 0.4893
## Region.fctrAfrica -1.848e+00 1.010e+00 -1.830 0.0695 .
## `Region.fctrWestern Pacific` 6.173e-01 1.062e+00 0.581 0.5621
## ChildMortality -1.699e-01 1.122e-02 -15.138 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.85 on 128 degrees of freedom
## Multiple R-squared: 0.915, Adjusted R-squared: 0.9083
## F-statistic: 137.7 on 10 and 128 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 Low.cor.X.lm lm
## feats
## 1 GNI.nonNA, CellularSubscribers.nonNA, Population, .rnorm, Region.fctr, ChildMortality
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 0.816 0.004
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.914963 3.06124 0.8688668 3.215729 0.9083195
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.8958836 0.643469 0.06005185rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 10 fit.models 7 0 28.564 42.492 13.928
## 11 fit.models 7 1 42.492 NA NAfit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn")## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 44.393 NA NA# Options:
# 1. rpart & rf manual tuning
# 2. rf without pca (default: with pca)
#stop(here); sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df
#glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df
# All X that is not user excluded
# if (glb_is_classification && glb_is_binomial) {
# model_id_pfx <- "Conditional.X"
# # indep_vars_vctr <- setdiff(names(glb_fitobs_df), union(glb_rsp_var, glb_exclude_vars_as_features))
# indep_vars_vctr <- subset(glb_feats_df, is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"]
# } else {
model_id_pfx <- "All.X"
indep_vars_vctr <- subset(glb_feats_df, !myNearZV &
(exclude.as.feat != 1))[, "id"]
# }
indep_vars_vctr <- myadjust_interaction_feats(indep_vars_vctr)
for (method in glb_models_method_vctr) {
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", method), major.inc=TRUE)
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indep_vars_vctr <- setdiff(indep_vars_vctr, c(".rnorm"))
model_id <- paste0(model_id_pfx, ".no.rnorm")
} else model_id <- model_id_pfx
if (method %in% c("glm")) # for a "robust" glm model
indep_vars_vctr <- setdiff(indep_vars_vctr, c(NULL
,"A.nchrs.log" # correlated to "S.*"
,"A.ndgts.log" # correlated to "S.*"
,"A.nuppr.log" # correlated to "S.*"
,"A.npnct01.log" # identical to "S.npnct01.log"
,"A.npnct03.log" # correlated to "S.npnct03.log"
,"A.npnct04.log" # correlated to "S.npnct04.log"
,"A.npnct06.log" # identical to "S.npnct06.log"
,"A.npnct07.log" # identical to "S.npnct07.log"
,"A.npnct08.log" # correlated to "S.npnct08.log"
,"A.npnct11.log" # correlated to "S.*"
,"A.npnct12.log" # correlated to "S.*"
,"S.npnct14.log" # correlated to "A.*"
,"A.npnct15.log" # correlated to "S.npnct15.log"
,"A.npnct16.log" # correlated to "S.npnct16.log"
,"A.npnct19.log" # correlated to "S.*"
,"A.npnct20.log" # identical to "S.npnct20.log"
,"A.npnct21.log" # correlated to "S.npnct21.log"
,"A.P.daily.clip.report" # identical to "S.*"
,"S.P.daily.clip.report" # identical to "H.*"
,"A.P.http" # correlated to "A.npnct14.log"
,"A.P.fashion.week" # identical to "S.*"
,"H.P.first.draft" # correlated to "H.T.first"
,"A.P.first.draft" # identical to "S.*"
,"A.P.metropolitan.diary.colon" # identical to "S.*"
,"A.P.year.colon" # identical to "S.P.year.colon"
))
ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
# If All.X.glm is less accurate than Low.Cor.X.glm
# check NA coefficients & filter appropriate terms in indep_vars_vctr
# if (method == "glm") {
# orig_glm <- glb_models_lst[[paste0(model_id, ".", model_method)]]$finalModel
# orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
# vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
# print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
# print(which.max(vif_orig_glm))
# print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
# glb_fitobs_df[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.nchrs.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.npnct14.log", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.T.scen", glb_feats_df$id, value=TRUE), ]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.P.first", glb_feats_df$id, value=TRUE), ]
# all.equal(glb_allobs_df$S.nuppr.log, glb_allobs_df$A.nuppr.log)
# all.equal(glb_allobs_df$S.npnct19.log, glb_allobs_df$A.npnct19.log)
# all.equal(glb_allobs_df$S.P.year.colon, glb_allobs_df$A.P.year.colon)
# all.equal(glb_allobs_df$S.T.share, glb_allobs_df$A.T.share)
# all.equal(glb_allobs_df$H.T.clip, glb_allobs_df$H.P.daily.clip.report)
# cor(glb_allobs_df$S.T.herald, glb_allobs_df$S.T.tribun)
# dsp_obs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
# dsp_obs(Abstract.contains="[Ss]hare", cols=("Abstract"), all=TRUE)
# subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
# corxx_mtrx <- cor(data.matrix(glb_allobs_df[, setdiff(names(glb_allobs_df), myfind_chr_cols_df(glb_allobs_df))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
# which.max(abs_corxx_mtrx["S.T.tribun", ])
# abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
# step_glm <- step(orig_glm)
# }
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ".cp.0"), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
}## label step_major step_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 44.393 44.407 0.014
## 2 fit.models_1_lm 2 0 44.407 NA NA
## [1] "fitting model: All.X.lm"
## [1] " indep_vars: Over60, LiteracyRate.nonNA, PrimarySchoolEnrollmentFemale.nonNA, GNI.nonNA, CellularSubscribers.nonNA, PrimarySchoolEnrollmentMale.nonNA, Population, .rnorm, Region.fctr, FertilityRate.nonNA, Under15, ChildMortality"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.5105 -1.6085 0.2761 1.4929 6.8723
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.651e+01 5.057e+00 15.130 < 2e-16
## Over60 1.079e-01 8.234e-02 1.311 0.1923
## LiteracyRate.nonNA -2.200e-02 2.635e-02 -0.835 0.4054
## PrimarySchoolEnrollmentFemale.nonNA -1.355e-01 7.816e-02 -1.733 0.0856
## GNI.nonNA 8.989e-05 2.091e-05 4.300 3.47e-05
## CellularSubscribers.nonNA -1.682e-03 7.912e-03 -0.213 0.8320
## PrimarySchoolEnrollmentMale.nonNA 1.393e-01 7.909e-02 1.761 0.0808
## Population 4.461e-07 1.602e-06 0.278 0.7812
## .rnorm 2.091e-01 2.786e-01 0.751 0.4543
## Region.fctrEurope 5.665e-01 1.278e+00 0.443 0.6584
## Region.fctrAmericas 2.288e+00 1.137e+00 2.013 0.0463
## `Region.fctrSouth-East Asia` 8.995e-01 1.310e+00 0.687 0.4935
## Region.fctrAfrica -1.945e+00 1.042e+00 -1.867 0.0643
## `Region.fctrWestern Pacific` 6.166e-01 1.096e+00 0.563 0.5746
## FertilityRate.nonNA 1.104e+00 6.505e-01 1.698 0.0921
## Under15 -1.392e-01 1.073e-01 -1.298 0.1969
## ChildMortality -1.935e-01 1.615e-02 -11.979 < 2e-16
##
## (Intercept) ***
## Over60
## LiteracyRate.nonNA
## PrimarySchoolEnrollmentFemale.nonNA .
## GNI.nonNA ***
## CellularSubscribers.nonNA
## PrimarySchoolEnrollmentMale.nonNA .
## Population
## .rnorm
## Region.fctrEurope
## Region.fctrAmericas *
## `Region.fctrSouth-East Asia`
## Region.fctrAfrica .
## `Region.fctrWestern Pacific`
## FertilityRate.nonNA .
## Under15
## ChildMortality ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.756 on 122 degrees of freedom
## Multiple R-squared: 0.9243, Adjusted R-squared: 0.9143
## F-statistic: 93.04 on 16 and 122 DF, p-value: < 2.2e-16
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.X.lm lm
## feats
## 1 Over60, LiteracyRate.nonNA, PrimarySchoolEnrollmentFemale.nonNA, GNI.nonNA, CellularSubscribers.nonNA, PrimarySchoolEnrollmentMale.nonNA, Population, .rnorm, Region.fctr, FertilityRate.nonNA, Under15, ChildMortality
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 0.828 0.006
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.9242529 3.218807 0.8765709 3.119837 0.9143188
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.8944665 0.3711841 0.03847592
## label step_major step_minor bgn end elapsed
## 2 fit.models_1_lm 2 0 44.407 46.642 2.235
## 3 fit.models_1_glm 3 0 46.642 NA NA
## [1] "fitting model: All.X.glm"
## [1] " indep_vars: Over60, LiteracyRate.nonNA, PrimarySchoolEnrollmentFemale.nonNA, GNI.nonNA, CellularSubscribers.nonNA, PrimarySchoolEnrollmentMale.nonNA, Population, .rnorm, Region.fctr, FertilityRate.nonNA, Under15, ChildMortality"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -7.5105 -1.6085 0.2761 1.4929 6.8723
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.651e+01 5.057e+00 15.130 < 2e-16
## Over60 1.079e-01 8.234e-02 1.311 0.1923
## LiteracyRate.nonNA -2.200e-02 2.635e-02 -0.835 0.4054
## PrimarySchoolEnrollmentFemale.nonNA -1.355e-01 7.816e-02 -1.733 0.0856
## GNI.nonNA 8.989e-05 2.091e-05 4.300 3.47e-05
## CellularSubscribers.nonNA -1.682e-03 7.912e-03 -0.213 0.8320
## PrimarySchoolEnrollmentMale.nonNA 1.393e-01 7.909e-02 1.761 0.0808
## Population 4.461e-07 1.602e-06 0.278 0.7812
## .rnorm 2.091e-01 2.786e-01 0.751 0.4543
## Region.fctrEurope 5.665e-01 1.278e+00 0.443 0.6584
## Region.fctrAmericas 2.288e+00 1.137e+00 2.013 0.0463
## `Region.fctrSouth-East Asia` 8.995e-01 1.310e+00 0.687 0.4935
## Region.fctrAfrica -1.945e+00 1.042e+00 -1.867 0.0643
## `Region.fctrWestern Pacific` 6.166e-01 1.096e+00 0.563 0.5746
## FertilityRate.nonNA 1.104e+00 6.505e-01 1.698 0.0921
## Under15 -1.392e-01 1.073e-01 -1.298 0.1969
## ChildMortality -1.935e-01 1.615e-02 -11.979 < 2e-16
##
## (Intercept) ***
## Over60
## LiteracyRate.nonNA
## PrimarySchoolEnrollmentFemale.nonNA .
## GNI.nonNA ***
## CellularSubscribers.nonNA
## PrimarySchoolEnrollmentMale.nonNA .
## Population
## .rnorm
## Region.fctrEurope
## Region.fctrAmericas *
## `Region.fctrSouth-East Asia`
## Region.fctrAfrica .
## `Region.fctrWestern Pacific`
## FertilityRate.nonNA .
## Under15
## ChildMortality ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 7.593608)
##
## Null deviance: 12230.43 on 138 degrees of freedom
## Residual deviance: 926.42 on 122 degrees of freedom
## AIC: 694.13
##
## Number of Fisher Scoring iterations: 2
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.X.glm glm
## feats
## 1 Over60, LiteracyRate.nonNA, PrimarySchoolEnrollmentFemale.nonNA, GNI.nonNA, CellularSubscribers.nonNA, PrimarySchoolEnrollmentMale.nonNA, Population, .rnorm, Region.fctr, FertilityRate.nonNA, Under15, ChildMortality
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 0.84 0.016
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB min.aic.fit
## 1 0.9242529 3.218807 0.8765709 3.119837 694.1276
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.8944665 0.3711841 0.03847592
## label step_major step_minor bgn end elapsed
## 3 fit.models_1_glm 3 0 46.642 48.936 2.294
## 4 fit.models_1_rpart 4 0 48.936 NA NA
## [1] "fitting model: All.X.no.rnorm.rpart"
## [1] " indep_vars: Over60, LiteracyRate.nonNA, PrimarySchoolEnrollmentFemale.nonNA, GNI.nonNA, CellularSubscribers.nonNA, PrimarySchoolEnrollmentMale.nonNA, Population, Region.fctr, FertilityRate.nonNA, Under15, ChildMortality"## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0797 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 139
##
## CP nsplit rel error
## 1 0.69808077 0 1.0000000
## 2 0.10343202 1 0.3019192
## 3 0.07966077 2 0.1984872
##
## Variable importance
## ChildMortality FertilityRate.nonNA
## 25 16
## GNI.nonNA Under15
## 16 15
## Over60 CellularSubscribers.nonNA
## 13 13
## LiteracyRate.nonNA PrimarySchoolEnrollmentFemale.nonNA
## 2 1
##
## Node number 1: 139 observations, complexity param=0.6980808
## mean=69.67626, MSE=87.98872
## left son=2 (50 obs) right son=3 (89 obs)
## Primary splits:
## ChildMortality < 37.5 to the right, improve=0.6980808, (0 missing)
## Under15 < 36.2 to the right, improve=0.5848761, (0 missing)
## FertilityRate.nonNA < 3.13 to the right, improve=0.5695908, (0 missing)
## Over60 < 6.325 to the left, improve=0.5129444, (0 missing)
## Region.fctrAfrica < 0.5 to the right, improve=0.4975190, (0 missing)
## Surrogate splits:
## GNI.nonNA < 3615 to the left, agree=0.899, adj=0.72, (0 split)
## FertilityRate.nonNA < 3.13 to the right, agree=0.878, adj=0.66, (0 split)
## Under15 < 34.88 to the right, agree=0.871, adj=0.64, (0 split)
## CellularSubscribers.nonNA < 69.355 to the left, agree=0.849, adj=0.58, (0 split)
## Over60 < 5.78 to the left, agree=0.835, adj=0.54, (0 split)
##
## Node number 2: 50 observations, complexity param=0.103432
## mean=59.22, MSE=38.4916
## left son=4 (22 obs) right son=5 (28 obs)
## Primary splits:
## ChildMortality < 78.65 to the right, improve=0.6572958, (0 missing)
## Under15 < 36.67 to the right, improve=0.4353715, (0 missing)
## FertilityRate.nonNA < 3.44 to the right, improve=0.3654708, (0 missing)
## Over60 < 6.345 to the left, improve=0.3498308, (0 missing)
## Region.fctrAfrica < 0.5 to the right, improve=0.3398993, (0 missing)
## Surrogate splits:
## LiteracyRate.nonNA < 56.5 to the left, agree=0.80, adj=0.545, (0 split)
## FertilityRate.nonNA < 4.67 to the right, agree=0.76, adj=0.455, (0 split)
## PrimarySchoolEnrollmentFemale.nonNA < 77.1 to the left, agree=0.72, adj=0.364, (0 split)
## Under15 < 36.67 to the right, agree=0.72, adj=0.364, (0 split)
## Over60 < 5.145 to the left, agree=0.68, adj=0.273, (0 split)
##
## Node number 3: 89 observations
## mean=75.55056, MSE=19.86542
##
## Node number 4: 22 observations
## mean=53.54545, MSE=13.15702
##
## Node number 5: 28 observations
## mean=63.67857, MSE=13.21811
##
## n= 139
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 139 12230.4300 69.67626
## 2) ChildMortality>=37.5 50 1924.5800 59.22000
## 4) ChildMortality>=78.65 22 289.4545 53.54545 *
## 5) ChildMortality< 78.65 28 370.1071 63.67857 *
## 3) ChildMortality< 37.5 89 1768.0220 75.55056 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.X.no.rnorm.rpart rpart
## feats
## 1 Over60, LiteracyRate.nonNA, PrimarySchoolEnrollmentFemale.nonNA, GNI.nonNA, CellularSubscribers.nonNA, PrimarySchoolEnrollmentMale.nonNA, Population, Region.fctr, FertilityRate.nonNA, Under15, ChildMortality
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 0.903 0.017
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.8015128 4.325582 0.764756 4.307076 0.8033837
## min.RMSESD.fit max.RsquaredSD.fit
## 1 0.6087925 0.07876564
## label step_major step_minor bgn end elapsed
## 4 fit.models_1_rpart 4 0 48.936 51.687 2.751
## 5 fit.models_1_rf 5 0 51.687 NA NA
## [1] "fitting model: All.X.no.rnorm.rf"
## [1] " indep_vars: Over60, LiteracyRate.nonNA, PrimarySchoolEnrollmentFemale.nonNA, GNI.nonNA, CellularSubscribers.nonNA, PrimarySchoolEnrollmentMale.nonNA, Population, Region.fctr, FertilityRate.nonNA, Under15, ChildMortality"## Loading required package: randomForest
## randomForest 4.6-10
## Type rfNews() to see new features/changes/bug fixes.
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 15 on full training set## Warning in myfit_mdl(model_id = model_id, model_method = method,
## indep_vars_vctr = indep_vars_vctr, : model's bestTune found at an extreme
## of tuneGrid for parameter: mtry
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 139 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 139 -none- numeric
## importance 15 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 139 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 15 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 1 -none- logical
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## model_id model_method
## 1 All.X.no.rnorm.rf rf
## feats
## 1 Over60, LiteracyRate.nonNA, PrimarySchoolEnrollmentFemale.nonNA, GNI.nonNA, CellularSubscribers.nonNA, PrimarySchoolEnrollmentMale.nonNA, Population, Region.fctr, FertilityRate.nonNA, Under15, ChildMortality
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 2.156 0.388
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.9849445 2.951944 0.8806828 3.066581 0.9125122
## min.RMSESD.fit max.RsquaredSD.fit
## 1 0.3147728 0.03773599# User specified
# easier to exclude features
#model_id_pfx <- "";
# indep_vars_vctr <- setdiff(names(glb_fitobs_df),
# union(union(glb_rsp_var, glb_exclude_vars_as_features),
# c("<feat1_name>", "<feat2_name>")))
# method <- ""
# easier to include features
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df
#table(glb_allobs_df$myCategory, glb_allobs_df$H.P.readers.respond, glb_allobs_df[, glb_rsp_var], useNA="ifany")
#model_id <- "Rank9.2"; indep_vars_vctr <- c(NULL
# ,"<feat1>"
# ,"<feat1>*<feat2>"
# ,"<feat1>:<feat2>"
# )
# for (method in c("bayesglm")) {
# ret_lst <- myfit_mdl(model_id=model_id, model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
# # csm_mdl_id <- paste0(model_id, ".", method)
# # csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(model_id, ".", method)]]); print(head(csm_featsimp_df))
# }
#csm_featsimp_df[grepl("H.npnct19.log", row.names(csm_featsimp_df)), , FALSE]
#csm_OOBobs_df <- glb_get_predictions(glb_OOBobs_df, mdl_id=csm_mdl_id, rsp_var_out=glb_rsp_var_out, prob_threshold_def=glb_models_df[glb_models_df$model_id == csm_mdl_id, "opt.prob.threshold.OOB"])
#print(sprintf("%s OOB confusion matrix & accuracy: ", csm_mdl_id)); print(t(confusionMatrix(csm_OOBobs_df[, paste0(glb_rsp_var_out, csm_mdl_id)], csm_OOBobs_df[, glb_rsp_var])$table))
#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$importance)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$importance)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]
#print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id)); print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)], glb_OOBobs_df[, glb_rsp_var])$table))
# User specified bivariate models
# indep_vars_vctr_lst <- list()
# for (feat in setdiff(names(glb_fitobs_df),
# union(glb_rsp_var, glb_exclude_vars_as_features)))
# indep_vars_vctr_lst[["feat"]] <- feat
# User specified combinatorial models
# indep_vars_vctr_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(model_id=paste0(model_id_pfx, ""), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df,
# model_loss_mtrx=glb_model_metric_terms,
# model_summaryFunction=glb_model_metric_smmry,
# model_metric=glb_model_metric,
# model_metric_maximize=glb_model_metric_maximize)
# Simplify a model
# fit_df <- glb_fitobs_df; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glb_fitobs_df, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glb_model_metric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## model_id model_method
## MFO.lm MFO.lm lm
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart Max.cor.Y.rpart rpart
## Max.cor.Y.lm Max.cor.Y.lm lm
## Interact.High.cor.Y.lm Interact.High.cor.Y.lm lm
## Low.cor.X.lm Low.cor.X.lm lm
## All.X.lm All.X.lm lm
## All.X.glm All.X.glm glm
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart rpart
## All.X.no.rnorm.rf All.X.no.rnorm.rf rf
## feats
## MFO.lm .rnorm
## Max.cor.Y.cv.0.rpart ChildMortality, GNI.nonNA
## Max.cor.Y.cv.0.cp.0.rpart ChildMortality, GNI.nonNA
## Max.cor.Y.rpart ChildMortality, GNI.nonNA
## Max.cor.Y.lm ChildMortality, GNI.nonNA
## Interact.High.cor.Y.lm ChildMortality, GNI.nonNA, ChildMortality:Under15, ChildMortality:ChildMortality, ChildMortality:PrimarySchoolEnrollmentFemale.nonNA
## Low.cor.X.lm GNI.nonNA, CellularSubscribers.nonNA, Population, .rnorm, Region.fctr, ChildMortality
## All.X.lm Over60, LiteracyRate.nonNA, PrimarySchoolEnrollmentFemale.nonNA, GNI.nonNA, CellularSubscribers.nonNA, PrimarySchoolEnrollmentMale.nonNA, Population, .rnorm, Region.fctr, FertilityRate.nonNA, Under15, ChildMortality
## All.X.glm Over60, LiteracyRate.nonNA, PrimarySchoolEnrollmentFemale.nonNA, GNI.nonNA, CellularSubscribers.nonNA, PrimarySchoolEnrollmentMale.nonNA, Population, .rnorm, Region.fctr, FertilityRate.nonNA, Under15, ChildMortality
## All.X.no.rnorm.rpart Over60, LiteracyRate.nonNA, PrimarySchoolEnrollmentFemale.nonNA, GNI.nonNA, CellularSubscribers.nonNA, PrimarySchoolEnrollmentMale.nonNA, Population, Region.fctr, FertilityRate.nonNA, Under15, ChildMortality
## All.X.no.rnorm.rf Over60, LiteracyRate.nonNA, PrimarySchoolEnrollmentFemale.nonNA, GNI.nonNA, CellularSubscribers.nonNA, PrimarySchoolEnrollmentMale.nonNA, Population, Region.fctr, FertilityRate.nonNA, Under15, ChildMortality
## max.nTuningRuns min.elapsedtime.everything
## MFO.lm 0 0.438
## Max.cor.Y.cv.0.rpart 0 0.511
## Max.cor.Y.cv.0.cp.0.rpart 0 0.424
## Max.cor.Y.rpart 3 1.127
## Max.cor.Y.lm 1 0.817
## Interact.High.cor.Y.lm 1 0.820
## Low.cor.X.lm 1 0.816
## All.X.lm 1 0.828
## All.X.glm 1 0.840
## All.X.no.rnorm.rpart 3 0.903
## All.X.no.rnorm.rf 3 2.156
## min.elapsedtime.final max.R.sq.fit min.RMSE.fit
## MFO.lm 0.004 0.001105554 9.375044
## Max.cor.Y.cv.0.rpart 0.009 0.000000000 9.380230
## Max.cor.Y.cv.0.cp.0.rpart 0.007 0.939870337 2.300159
## Max.cor.Y.rpart 0.009 0.801512798 4.325582
## Max.cor.Y.lm 0.003 0.900750962 3.003298
## Interact.High.cor.Y.lm 0.003 0.909657884 2.981268
## Low.cor.X.lm 0.004 0.914963005 3.061240
## All.X.lm 0.006 0.924252863 3.218807
## All.X.glm 0.016 0.924252863 3.218807
## All.X.no.rnorm.rpart 0.017 0.801512798 4.325582
## All.X.no.rnorm.rf 0.388 0.984944475 2.951944
## max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## MFO.lm 0.009390806 8.838414 -0.006185647
## Max.cor.Y.cv.0.rpart 0.000000000 8.880209 NA
## Max.cor.Y.cv.0.cp.0.rpart 0.865968304 3.251075 NA
## Max.cor.Y.rpart 0.764755963 4.307076 NA
## Max.cor.Y.lm 0.855491202 3.375750 0.899291417
## Interact.High.cor.Y.lm 0.845856653 3.486467 0.906961104
## Low.cor.X.lm 0.868866804 3.215729 0.908319490
## All.X.lm 0.876570916 3.119837 0.914318813
## All.X.glm 0.876570916 3.119837 NA
## All.X.no.rnorm.rpart 0.764755963 4.307076 NA
## All.X.no.rnorm.rf 0.880682838 3.066581 NA
## max.Rsquared.fit min.RMSESD.fit
## MFO.lm NA NA
## Max.cor.Y.cv.0.rpart NA NA
## Max.cor.Y.cv.0.cp.0.rpart NA NA
## Max.cor.Y.rpart 0.8033837 0.6087925
## Max.cor.Y.lm 0.9058445 0.7711282
## Interact.High.cor.Y.lm 0.9116481 0.6464750
## Low.cor.X.lm 0.8958836 0.6434690
## All.X.lm 0.8944665 0.3711841
## All.X.glm 0.8944665 0.3711841
## All.X.no.rnorm.rpart 0.8033837 0.6087925
## All.X.no.rnorm.rf 0.9125122 0.3147728
## max.RsquaredSD.fit min.aic.fit
## MFO.lm NA NA
## Max.cor.Y.cv.0.rpart NA NA
## Max.cor.Y.cv.0.cp.0.rpart NA NA
## Max.cor.Y.rpart 0.07876564 NA
## Max.cor.Y.lm 0.05023664 NA
## Interact.High.cor.Y.lm 0.03793371 NA
## Low.cor.X.lm 0.06005185 NA
## All.X.lm 0.03847592 NA
## All.X.glm 0.03847592 694.1276
## All.X.no.rnorm.rpart 0.07876564 NA
## All.X.no.rnorm.rf 0.03773599 NArm(ret_lst)
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end",
major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 5 fit.models_1_rf 5 0 51.687 55.369 3.682
## 6 fit.models_1_end 6 0 55.369 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 11 fit.models 7 1 42.492 55.374 12.883
## 12 fit.models 7 2 55.375 NA NAif (!is.null(glb_model_metric_smmry)) {
stats_df <- glb_models_df[, "model_id", FALSE]
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_fitobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "fit",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_OOBobs_df, glb_rsp_var,
glb_rsp_var_out, model_id, "OOB",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
# tmp_models_df <- orderBy(~model_id, glb_models_df)
# rownames(tmp_models_df) <- seq(1, nrow(tmp_models_df))
# all.equal(subset(tmp_models_df[, names(stats_df)], model_id != "Random.myrandom_classfr"),
# subset(stats_df, model_id != "Random.myrandom_classfr"))
# print(subset(tmp_models_df[, names(stats_df)], model_id != "Random.myrandom_classfr")[, c("model_id", "max.Accuracy.fit")])
# print(subset(stats_df, model_id != "Random.myrandom_classfr")[, c("model_id", "max.Accuracy.fit")])
print("Merging following data into glb_models_df:")
print(stats_mrg_df <- stats_df[, c(1, grep(glb_model_metric, names(stats_df)))])
print(tmp_models_df <- orderBy(~model_id, glb_models_df[, c("model_id", grep(glb_model_metric, names(stats_df), value=TRUE))]))
tmp2_models_df <- glb_models_df[, c("model_id", setdiff(names(glb_models_df), grep(glb_model_metric, names(stats_df), value=TRUE)))]
tmp3_models_df <- merge(tmp2_models_df, stats_mrg_df, all.x=TRUE, sort=FALSE)
print(tmp3_models_df)
print(names(tmp3_models_df))
print(glb_models_df <- subset(tmp3_models_df, select=-model_id.1))
}
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## model_id model_method
## MFO.lm MFO.lm lm
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart rpart
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart rpart
## Max.cor.Y.rpart Max.cor.Y.rpart rpart
## Max.cor.Y.lm Max.cor.Y.lm lm
## Interact.High.cor.Y.lm Interact.High.cor.Y.lm lm
## Low.cor.X.lm Low.cor.X.lm lm
## All.X.lm All.X.lm lm
## All.X.glm All.X.glm glm
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart rpart
## All.X.no.rnorm.rf All.X.no.rnorm.rf rf
## feats
## MFO.lm .rnorm
## Max.cor.Y.cv.0.rpart ChildMortality, GNI.nonNA
## Max.cor.Y.cv.0.cp.0.rpart ChildMortality, GNI.nonNA
## Max.cor.Y.rpart ChildMortality, GNI.nonNA
## Max.cor.Y.lm ChildMortality, GNI.nonNA
## Interact.High.cor.Y.lm ChildMortality, GNI.nonNA, ChildMortality:Under15, ChildMortality:ChildMortality, ChildMortality:PrimarySchoolEnrollmentFemale.nonNA
## Low.cor.X.lm GNI.nonNA, CellularSubscribers.nonNA, Population, .rnorm, Region.fctr, ChildMortality
## All.X.lm Over60, LiteracyRate.nonNA, PrimarySchoolEnrollmentFemale.nonNA, GNI.nonNA, CellularSubscribers.nonNA, PrimarySchoolEnrollmentMale.nonNA, Population, .rnorm, Region.fctr, FertilityRate.nonNA, Under15, ChildMortality
## All.X.glm Over60, LiteracyRate.nonNA, PrimarySchoolEnrollmentFemale.nonNA, GNI.nonNA, CellularSubscribers.nonNA, PrimarySchoolEnrollmentMale.nonNA, Population, .rnorm, Region.fctr, FertilityRate.nonNA, Under15, ChildMortality
## All.X.no.rnorm.rpart Over60, LiteracyRate.nonNA, PrimarySchoolEnrollmentFemale.nonNA, GNI.nonNA, CellularSubscribers.nonNA, PrimarySchoolEnrollmentMale.nonNA, Population, Region.fctr, FertilityRate.nonNA, Under15, ChildMortality
## All.X.no.rnorm.rf Over60, LiteracyRate.nonNA, PrimarySchoolEnrollmentFemale.nonNA, GNI.nonNA, CellularSubscribers.nonNA, PrimarySchoolEnrollmentMale.nonNA, Population, Region.fctr, FertilityRate.nonNA, Under15, ChildMortality
## max.nTuningRuns max.R.sq.fit max.R.sq.OOB
## MFO.lm 0 0.001105554 0.009390806
## Max.cor.Y.cv.0.rpart 0 0.000000000 0.000000000
## Max.cor.Y.cv.0.cp.0.rpart 0 0.939870337 0.865968304
## Max.cor.Y.rpart 3 0.801512798 0.764755963
## Max.cor.Y.lm 1 0.900750962 0.855491202
## Interact.High.cor.Y.lm 1 0.909657884 0.845856653
## Low.cor.X.lm 1 0.914963005 0.868866804
## All.X.lm 1 0.924252863 0.876570916
## All.X.glm 1 0.924252863 0.876570916
## All.X.no.rnorm.rpart 3 0.801512798 0.764755963
## All.X.no.rnorm.rf 3 0.984944475 0.880682838
## max.Adj.R.sq.fit max.Rsquared.fit
## MFO.lm -0.006185647 NA
## Max.cor.Y.cv.0.rpart NA NA
## Max.cor.Y.cv.0.cp.0.rpart NA NA
## Max.cor.Y.rpart NA 0.8033837
## Max.cor.Y.lm 0.899291417 0.9058445
## Interact.High.cor.Y.lm 0.906961104 0.9116481
## Low.cor.X.lm 0.908319490 0.8958836
## All.X.lm 0.914318813 0.8944665
## All.X.glm NA 0.8944665
## All.X.no.rnorm.rpart NA 0.8033837
## All.X.no.rnorm.rf NA 0.9125122
## inv.elapsedtime.everything inv.elapsedtime.final
## MFO.lm 2.2831050 250.00000
## Max.cor.Y.cv.0.rpart 1.9569472 111.11111
## Max.cor.Y.cv.0.cp.0.rpart 2.3584906 142.85714
## Max.cor.Y.rpart 0.8873114 111.11111
## Max.cor.Y.lm 1.2239902 333.33333
## Interact.High.cor.Y.lm 1.2195122 333.33333
## Low.cor.X.lm 1.2254902 250.00000
## All.X.lm 1.2077295 166.66667
## All.X.glm 1.1904762 62.50000
## All.X.no.rnorm.rpart 1.1074197 58.82353
## All.X.no.rnorm.rf 0.4638219 2.57732
## inv.RMSE.fit inv.RMSE.OOB inv.aic.fit
## MFO.lm 0.1066662 0.1131425 NA
## Max.cor.Y.cv.0.rpart 0.1066072 0.1126100 NA
## Max.cor.Y.cv.0.cp.0.rpart 0.4347525 0.3075906 NA
## Max.cor.Y.rpart 0.2311828 0.2321761 NA
## Max.cor.Y.lm 0.3329673 0.2962304 NA
## Interact.High.cor.Y.lm 0.3354278 0.2868233 NA
## Low.cor.X.lm 0.3266650 0.3109714 NA
## All.X.lm 0.3106741 0.3205295 NA
## All.X.glm 0.3106741 0.3205295 0.001440657
## All.X.no.rnorm.rpart 0.2311828 0.2321761 NA
## All.X.no.rnorm.rf 0.3387598 0.3260961 NAprint(myplot_radar(radar_inp_df=plt_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 11. Consider specifying shapes manually. if you must have them.## Warning in loop_apply(n, do.ply): Removed 8 rows containing missing values
## (geom_path).## Warning in loop_apply(n, do.ply): Removed 58 rows containing missing values
## (geom_point).## Warning in loop_apply(n, do.ply): Removed 19 rows containing missing values
## (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 11. Consider specifying shapes manually. if you must have them.
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(model_id %in% grep("random|MFO", plt_models_df$model_id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## [1] "var:min.RMSESD.fit"
## [1] "var:max.RsquaredSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "model_id", FALSE]
pltCI_models_df <- glb_models_df[, "model_id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="model_id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="model_id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
#print(mltdCI_models_df)
# castCI_models_df <- dcast(mltdCI_models_df, value ~ type, fun.aggregate=sum)
# print(castCI_models_df)
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("model_id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("model_id", "model_method")],
all.x=TRUE)
png(paste0(glb_out_pfx, "models_bar.png"), width=480*3, height=480*2)
print(gp <- myplot_bar(mltd_models_df, "model_id", "value", colorcol_name="model_method") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=model_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))## Warning in loop_apply(n, do.ply): Removed 3 rows containing missing values
## (position_stack).dev.off()## quartz_off_screen
## 2print(gp)## Warning in loop_apply(n, do.ply): Removed 3 rows containing missing values
## (position_stack).
# used for console inspection
model_evl_terms <- c(NULL)
for (metric in glb_model_evl_criteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse=" "))
dsp_models_cols <- c("model_id", glb_model_evl_criteria)
if (glb_is_classification && glb_is_binomial)
dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, dsp_models_cols])## model_id min.RMSE.OOB
## All.X.no.rnorm.rf All.X.no.rnorm.rf 3.066581
## All.X.lm All.X.lm 3.119837
## All.X.glm All.X.glm 3.119837
## Low.cor.X.lm Low.cor.X.lm 3.215729
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart 3.251075
## Max.cor.Y.lm Max.cor.Y.lm 3.375750
## Interact.High.cor.Y.lm Interact.High.cor.Y.lm 3.486467
## Max.cor.Y.rpart Max.cor.Y.rpart 4.307076
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart 4.307076
## MFO.lm MFO.lm 8.838414
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart 8.880209
## max.R.sq.OOB max.Adj.R.sq.fit
## All.X.no.rnorm.rf 0.880682838 NA
## All.X.lm 0.876570916 0.914318813
## All.X.glm 0.876570916 NA
## Low.cor.X.lm 0.868866804 0.908319490
## Max.cor.Y.cv.0.cp.0.rpart 0.865968304 NA
## Max.cor.Y.lm 0.855491202 0.899291417
## Interact.High.cor.Y.lm 0.845856653 0.906961104
## Max.cor.Y.rpart 0.764755963 NA
## All.X.no.rnorm.rpart 0.764755963 NA
## MFO.lm 0.009390806 -0.006185647
## Max.cor.Y.cv.0.rpart 0.000000000 NAprint(myplot_radar(radar_inp_df=dsp_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 11. Consider specifying shapes manually. if you must have them.## Warning in loop_apply(n, do.ply): Removed 5 rows containing missing values
## (geom_path).## Warning in loop_apply(n, do.ply): Removed 20 rows containing missing values
## (geom_point).## Warning in loop_apply(n, do.ply): Removed 6 rows containing missing values
## (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 11. Consider specifying shapes manually. if you must have them.
print("Metrics used for model selection:"); print(model_sel_frmla)## [1] "Metrics used for model selection:"## ~+min.RMSE.OOB - max.R.sq.OOB - max.Adj.R.sq.fitprint(sprintf("Best model id: %s", dsp_models_df[1, "model_id"]))## [1] "Best model id: All.X.no.rnorm.rf"if (is.null(glb_sel_mdl_id)) {
glb_sel_mdl_id <- dsp_models_df[1, "model_id"]
if (glb_sel_mdl_id == "Interact.High.cor.Y.glm") {
warning("glb_sel_mdl_id: Interact.High.cor.Y.glm; myextract_mdl_feats does not currently support interaction terms")
glb_sel_mdl_id <- dsp_models_df[2, "model_id"]
}
} else
print(sprintf("User specified selection: %s", glb_sel_mdl_id))
myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 139 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 139 -none- numeric
## importance 15 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 139 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 15 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 1 -none- logical## [1] TRUE# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
glb_get_predictions <- function(df, mdl_id, rsp_var_out, prob_threshold_def=NULL) {
mdl <- glb_models_lst[[mdl_id]]
rsp_var_out <- paste0(rsp_var_out, mdl_id)
if (glb_is_regression) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
print(myplot_scatter(df, glb_rsp_var, rsp_var_out, smooth=TRUE))
df[, paste0(rsp_var_out, ".err")] <-
abs(df[, rsp_var_out] - df[, glb_rsp_var])
print(head(orderBy(reformulate(c("-", paste0(rsp_var_out, ".err"))),
df)))
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$model_id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")[, 2]
df[, rsp_var_out] <-
factor(levels(df[, glb_rsp_var])[
(df[, paste0(rsp_var_out, ".prob")] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# prediction stats already reported by myfit_mdl ???
}
if (glb_is_classification && !glb_is_binomial) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
}
return(df)
}
glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
rsp_var_out=glb_rsp_var_out)
## Country Region Population Under15 Over60
## 129 Pakistan Eastern Mediterranean 179000 34.31 6.44
## 98 Libya Eastern Mediterranean 6155 29.45 6.96
## 16 Belarus Europe 9405 15.10 19.31
## 91 Kuwait Eastern Mediterranean 3250 24.90 3.80
## 142 Russian Federation Europe 143000 15.45 18.60
## 52 Dominican Republic Americas 10277 30.53 8.97
## FertilityRate LifeExpectancy ChildMortality CellularSubscribers
## 129 3.35 67 85.9 61.61
## 98 2.47 65 15.4 155.70
## 16 1.47 71 5.2 111.88
## 91 2.65 80 11.0 175.09
## 142 1.51 69 10.3 179.31
## 52 2.55 73 27.1 87.22
## LiteracyRate GNI PrimarySchoolEnrollmentMale
## 129 NA 2870 81.3
## 98 89.2 NA NA
## 16 NA 14460 NA
## 91 NA NA NA
## 142 99.6 20560 NA
## 52 89.5 9420 95.5
## PrimarySchoolEnrollmentFemale .src .rnorm FertilityRate.nonNA
## 129 66.5 Test 0.07455118 3.35
## 98 NA Test 0.68430943 2.47
## 16 NA Test 0.48545998 1.47
## 91 NA Test 0.96252797 2.65
## 142 NA Test 0.73649596 1.51
## 52 90.4 Test 1.08079950 2.55
## CellularSubscribers.nonNA LiteracyRate.nonNA GNI.nonNA
## 129 61.61 56.2 2870
## 98 155.70 89.2 10440
## 16 111.88 93.2 14460
## 91 175.09 96.3 3640
## 142 179.31 99.6 20560
## 52 87.22 89.5 9420
## PrimarySchoolEnrollmentMale.nonNA PrimarySchoolEnrollmentFemale.nonNA
## 129 81.3 66.5
## 98 98.9 99.3
## 16 96.5 96.5
## 91 96.5 97.0
## 142 97.0 97.3
## 52 95.5 90.4
## Region.fctr LifeExpectancy.predict.All.X.no.rnorm.rf
## 129 Eastern Mediterranean 55.39683
## 98 Eastern Mediterranean 73.97303
## 16 Europe 78.17203
## 91 Eastern Mediterranean 73.98070
## 142 Europe 74.07800
## 52 Americas 67.95067
## LifeExpectancy.predict.All.X.no.rnorm.rf.err
## 129 11.603167
## 98 8.973033
## 16 7.172033
## 91 6.019300
## 142 5.078000
## 52 5.049333predct_accurate_var_name <- paste0(glb_rsp_var_out, glb_sel_mdl_id, ".accurate")
glb_OOBobs_df[, predct_accurate_var_name] <-
(glb_OOBobs_df[, glb_rsp_var] ==
glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)])
#stop(here"); sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df
glb_featsimp_df <-
myget_feats_importance(mdl=glb_sel_mdl, featsimp_df=NULL)
glb_featsimp_df[, paste0(glb_sel_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## importance
## ChildMortality 100.00000000
## GNI.nonNA 2.43788756
## LiteracyRate.nonNA 0.92477967
## FertilityRate.nonNA 0.71952386
## Over60 0.69741081
## Population 0.61068053
## PrimarySchoolEnrollmentMale.nonNA 0.53976660
## Under15 0.52805249
## Region.fctrAfrica 0.52203704
## PrimarySchoolEnrollmentFemale.nonNA 0.51930063
## CellularSubscribers.nonNA 0.46309748
## Region.fctrAmericas 0.12263012
## Region.fctrSouth-East Asia 0.03026750
## Region.fctrWestern Pacific 0.02854844
## Region.fctrEurope 0.02524768
## All.X.no.rnorm.rf.importance
## ChildMortality 100.00000000
## GNI.nonNA 2.43788756
## LiteracyRate.nonNA 0.92477967
## FertilityRate.nonNA 0.71952386
## Over60 0.69741081
## Population 0.61068053
## PrimarySchoolEnrollmentMale.nonNA 0.53976660
## Under15 0.52805249
## Region.fctrAfrica 0.52203704
## PrimarySchoolEnrollmentFemale.nonNA 0.51930063
## CellularSubscribers.nonNA 0.46309748
## Region.fctrAmericas 0.12263012
## Region.fctrSouth-East Asia 0.03026750
## Region.fctrWestern Pacific 0.02854844
## Region.fctrEurope 0.02524768# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
featsimp_df <- glb_featsimp_df
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <- ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -importance.max, summaryBy(importance ~ feat + feat.interact,
data=featsimp_df, FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ", nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- paste0(glb_rsp_var_out, mdl_id)
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars=var,
measure.vars=c(glb_rsp_var, rsp_var_out))
# if (var == "<feat_name>") print(myplot_scatter(plot_df, var, "value",
# facet_colcol_name="variable") +
# geom_vline(xintercept=<divider_val>, linetype="dotted")) else
print(myplot_scatter(plot_df, var, "value", colorcol_name="variable",
facet_colcol_name="variable", jitter=TRUE) +
guides(color=FALSE))
}
if (glb_is_regression) {
if (nrow(featsimp_df) == 0)
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glb_id_vars)
# + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (nrow(featsimp_df) == 0)
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var,
rsp_var_out=rsp_var_out,
id_vars=glb_id_vars,
prob_threshold=prob_threshold)
# + geom_hline(yintercept=<divider_val>, linetype = "dotted")
)
}
}
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_OOBobs_df, mdl_id =
## glb_sel_mdl_id): Limiting important feature scatter plots to 5 out of 11
## Country Region Population Under15 Over60
## 129 Pakistan Eastern Mediterranean 179000 34.31 6.44
## 98 Libya Eastern Mediterranean 6155 29.45 6.96
## 16 Belarus Europe 9405 15.10 19.31
## 91 Kuwait Eastern Mediterranean 3250 24.90 3.80
## 142 Russian Federation Europe 143000 15.45 18.60
## FertilityRate LifeExpectancy ChildMortality CellularSubscribers
## 129 3.35 67 85.9 61.61
## 98 2.47 65 15.4 155.70
## 16 1.47 71 5.2 111.88
## 91 2.65 80 11.0 175.09
## 142 1.51 69 10.3 179.31
## LiteracyRate GNI PrimarySchoolEnrollmentMale
## 129 NA 2870 81.3
## 98 89.2 NA NA
## 16 NA 14460 NA
## 91 NA NA NA
## 142 99.6 20560 NA
## PrimarySchoolEnrollmentFemale .src .rnorm FertilityRate.nonNA
## 129 66.5 Test 0.07455118 3.35
## 98 NA Test 0.68430943 2.47
## 16 NA Test 0.48545998 1.47
## 91 NA Test 0.96252797 2.65
## 142 NA Test 0.73649596 1.51
## CellularSubscribers.nonNA LiteracyRate.nonNA GNI.nonNA
## 129 61.61 56.2 2870
## 98 155.70 89.2 10440
## 16 111.88 93.2 14460
## 91 175.09 96.3 3640
## 142 179.31 99.6 20560
## PrimarySchoolEnrollmentMale.nonNA PrimarySchoolEnrollmentFemale.nonNA
## 129 81.3 66.5
## 98 98.9 99.3
## 16 96.5 96.5
## 91 96.5 97.0
## 142 97.0 97.3
## Region.fctr LifeExpectancy.predict.All.X.no.rnorm.rf
## 129 Eastern Mediterranean 55.39683
## 98 Eastern Mediterranean 73.97303
## 16 Europe 78.17203
## 91 Eastern Mediterranean 73.98070
## 142 Europe 74.07800
## LifeExpectancy.predict.All.X.no.rnorm.rf.err
## 129 11.603167
## 98 8.973033
## 16 7.172033
## 91 6.019300
## 142 5.078000
## LifeExpectancy.predict.All.X.no.rnorm.rf.accurate .label
## 129 FALSE Pakistan
## 98 FALSE Libya
## 16 FALSE Belarus
## 91 FALSE Kuwait
## 142 FALSE Russian Federation
# gather predictions from models better than MFO.*
#mdl_id <- "Conditional.X.rf"
#mdl_id <- "Conditional.X.cp.0.rpart"
#mdl_id <- "Conditional.X.rpart"
# glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id,
# glb_rsp_var_out)
# print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, mdl_id)],
# glb_OOBobs_df[, glb_rsp_var])$table))
FN_OOB_ids <- c(4721, 4020, 693, 92)
print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])## [1] LifeExpectancy
## [2] LifeExpectancy.predict.All.X.no.rnorm.rf
## [3] LifeExpectancy.predict.All.X.no.rnorm.rf.err
## [4] LifeExpectancy.predict.All.X.no.rnorm.rf.accurate
## <0 rows> (or 0-length row.names)print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
glb_feats_df$id[1:5]])## [1] LiteracyRate PrimarySchoolEnrollmentFemale
## [3] Over60 GNI
## [5] LiteracyRate.nonNA
## <0 rows> (or 0-length row.names)print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
glb_txt_vars])## data frame with 0 columns and 0 rowswrite.csv(glb_OOBobs_df[, c(glb_id_vars,
grep(glb_rsp_var, names(glb_OOBobs_df), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glb_out_pfx, glb_sel_mdl_id), fixed=TRUE),
"_OOBobs.csv"), row.names=FALSE)
# print(glb_allobs_df[glb_allobs_df$UniqueID %in% FN_OOB_ids,
# glb_txt_vars])
# dsp_tbl(Headline.contains="[Ee]bola")
# sum(sel_obs(Headline.contains="[Ee]bola"))
# ftable(xtabs(Popular ~ NewsDesk.fctr, data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,]))
# xtabs(NewsDesk ~ Popular, #Popular ~ NewsDesk.fctr,
# data=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# exclude=NULL)
# print(mycreate_xtab_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular", "NewsDesk", "SectionName", "SubsectionName")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,], c("Popular")))
# print(mycreate_tbl_df(df=glb_allobs_df[sel_obs(Headline.contains="[Ee]bola") ,],
# tbl_col_names=c("Popular", "NewsDesk")))
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 12 fit.models 7 2 55.375 64.353 8.978
## 13 fit.models 7 3 64.353 NA NAprint(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## [1] "LifeExpectancy.predict.All.X.no.rnorm.rf"
## [2] "LifeExpectancy.predict.All.X.no.rnorm.rf.err"
## [3] "LifeExpectancy.predict.All.X.no.rnorm.rf.accurate"for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_sel_mdl, glb_sel_mdl_id,
glb_model_type,
file=paste0(glb_out_pfx, "selmdl_dsk.RData"))
#load(paste0(glb_out_pfx, "selmdl_dsk.RData"))
rm(ret_lst)## Warning in rm(ret_lst): object 'ret_lst' not foundreplay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 13 fit.models 7 3 64.353 67.65 3.298
## 14 fit.data.training 8 0 67.651 NA NAStep 8.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
# To create specific models
# glb_fin_mdl_id <- NULL; glb_fin_mdl <- NULL;
# glb_sel_mdl_id <- "Conditional.X.cp.0.rpart";
# glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]]; print(glb_sel_mdl)
if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_sel_mdl
} else {
# print(mdl_feats_df <- myextract_mdl_feats(sel_mdl=glb_sel_mdl,
# entity_df=glb_fitobs_df))
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the model_id
model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
tune_finmdl_df <- NULL
if (nrow(glb_sel_mdl$bestTune) > 0) {
for (param in names(glb_sel_mdl$bestTune)) {
#print(sprintf("param: %s", param))
if (glb_sel_mdl$bestTune[1, param] != "none")
tune_finmdl_df <- rbind(tune_finmdl_df,
data.frame(parameter=param,
min=glb_sel_mdl$bestTune[1, param],
max=glb_sel_mdl$bestTune[1, param],
by=1)) # by val does not matter
}
}
# Sync with parameters in mydsutils.R
require(gdata)
ret_lst <- myfit_mdl(model_id="Final", model_method=model_method,
indep_vars_vctr=trim(unlist(strsplit(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"feats"], "[,]"))),
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnobs_df, OOB_df=NULL,
n_cv_folds=glb_n_cv_folds, tune_models_df=tune_finmdl_df,
# Automate from here
# Issues if glb_sel_mdl$method == "rf" b/c trainControl is "oob"; not "cv"
model_loss_mtrx=glb_model_metric_terms,
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize)
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "model_id"]
}## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
##
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
##
## Attaching package: 'gdata'
##
## The following object is masked from 'package:randomForest':
##
## combine
##
## The following object is masked from 'package:stats':
##
## nobs
##
## The following object is masked from 'package:utils':
##
## object.size## [1] "fitting model: Final.rf"
## [1] " indep_vars: Over60, LiteracyRate.nonNA, PrimarySchoolEnrollmentFemale.nonNA, GNI.nonNA, CellularSubscribers.nonNA, PrimarySchoolEnrollmentMale.nonNA, Population, Region.fctr, FertilityRate.nonNA, Under15, ChildMortality"
## Aggregating results
## Fitting final model on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 139 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 139 -none- numeric
## importance 15 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 139 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 15 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 1 -none- logical
## [1] " calling mypredict_mdl for fit:"
## model_id model_method
## 1 Final.rf rf
## feats
## 1 Over60, LiteracyRate.nonNA, PrimarySchoolEnrollmentFemale.nonNA, GNI.nonNA, CellularSubscribers.nonNA, PrimarySchoolEnrollmentMale.nonNA, Population, Region.fctr, FertilityRate.nonNA, Under15, ChildMortality
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.718 0.385
## max.R.sq.fit min.RMSE.fit max.Rsquared.fit min.RMSESD.fit
## 1 0.9849445 2.945254 0.912703 0.2981539
## max.RsquaredSD.fit
## 1 0.03665828rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor bgn end elapsed
## 14 fit.data.training 8 0 67.651 71.905 4.255
## 15 fit.data.training 8 1 71.906 NA NAglb_trnobs_df <- glb_get_predictions(df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "opt.prob.threshold.OOB"], NULL))
## Country Region Population Under15 Over60
## 166 Swaziland Africa 1231 38.05 5.34
## 107 Marshall Islands Western Pacific 53 30.10 8.84
## 160 South Africa Africa 52386 29.53 8.44
## 88 Kazakhstan Europe 16271 25.46 10.04
## 73 Guyana Americas 795 36.77 5.18
## 1 Afghanistan Eastern Mediterranean 29825 47.42 3.82
## FertilityRate LifeExpectancy ChildMortality CellularSubscribers
## 166 3.48 50 79.7 63.70
## 107 NA 60 37.9 NA
## 160 2.44 58 44.6 126.83
## 88 2.52 67 18.7 155.74
## 73 2.64 63 35.2 69.94
## 1 5.40 60 98.5 54.26
## LiteracyRate GNI PrimarySchoolEnrollmentMale
## 166 87.4 5930 NA
## 107 NA NA NA
## 160 NA 10710 NA
## 88 99.7 11250 NA
## 73 NA NA 82.4
## 1 NA 1140 NA
## PrimarySchoolEnrollmentFemale .src .rnorm FertilityRate.nonNA
## 166 NA Train 0.6179858 3.48
## 107 NA Train 0.3104807 2.53
## 160 NA Train 1.6756969 2.44
## 88 NA Train -1.0491770 2.52
## 73 85.9 Train 0.7690422 2.64
## 1 NA Train -0.3475426 5.40
## CellularSubscribers.nonNA LiteracyRate.nonNA GNI.nonNA
## 166 63.70 87.4 5930
## 107 104.55 89.5 540
## 160 126.83 84.5 10710
## 88 155.74 99.7 11250
## 73 69.94 82.6 5930
## 1 54.26 56.0 1140
## PrimarySchoolEnrollmentMale.nonNA PrimarySchoolEnrollmentFemale.nonNA
## 166 93.9 94.5
## 107 93.3 94.5
## 160 93.3 94.4
## 88 99.2 99.2
## 73 82.4 85.9
## 1 85.3 79.5
## Region.fctr LifeExpectancy.predict.Final.rf
## 166 Africa 53.72063
## 107 Western Pacific 63.21903
## 160 Africa 61.14077
## 88 Europe 70.03887
## 73 Americas 65.59923
## 1 Eastern Mediterranean 57.41310
## LifeExpectancy.predict.Final.rf.err
## 166 3.720633
## 107 3.219033
## 160 3.140767
## 88 3.038867
## 73 2.599233
## 1 2.586900sav_featsimp_df <- glb_featsimp_df
#glb_feats_df <- sav_feats_df
# glb_feats_df <- mymerge_feats_importance(feats_df=glb_feats_df, sel_mdl=glb_fin_mdl,
# entity_df=glb_trnobs_df)
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl, featsimp_df=glb_featsimp_df)
glb_featsimp_df[, paste0(glb_fin_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## All.X.no.rnorm.rf.importance
## ChildMortality 100.00000000
## GNI.nonNA 2.43788756
## LiteracyRate.nonNA 0.92477967
## FertilityRate.nonNA 0.71952386
## Over60 0.69741081
## Population 0.61068053
## PrimarySchoolEnrollmentMale.nonNA 0.53976660
## Under15 0.52805249
## Region.fctrAfrica 0.52203704
## PrimarySchoolEnrollmentFemale.nonNA 0.51930063
## CellularSubscribers.nonNA 0.46309748
## Region.fctrAmericas 0.12263012
## Region.fctrSouth-East Asia 0.03026750
## Region.fctrWestern Pacific 0.02854844
## Region.fctrEurope 0.02524768
## importance Final.rf.importance
## ChildMortality 100.00000000 100.00000000
## GNI.nonNA 2.43788756 2.43788756
## LiteracyRate.nonNA 0.92477967 0.92477967
## FertilityRate.nonNA 0.71952386 0.71952386
## Over60 0.69741081 0.69741081
## Population 0.61068053 0.61068053
## PrimarySchoolEnrollmentMale.nonNA 0.53976660 0.53976660
## Under15 0.52805249 0.52805249
## Region.fctrAfrica 0.52203704 0.52203704
## PrimarySchoolEnrollmentFemale.nonNA 0.51930063 0.51930063
## CellularSubscribers.nonNA 0.46309748 0.46309748
## Region.fctrAmericas 0.12263012 0.12263012
## Region.fctrSouth-East Asia 0.03026750 0.03026750
## Region.fctrWestern Pacific 0.02854844 0.02854844
## Region.fctrEurope 0.02524768 0.02524768if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id): Limiting important feature scatter plots to 5 out of 11
## Country Region Population Under15 Over60
## 166 Swaziland Africa 1231 38.05 5.34
## 107 Marshall Islands Western Pacific 53 30.10 8.84
## 160 South Africa Africa 52386 29.53 8.44
## 88 Kazakhstan Europe 16271 25.46 10.04
## 73 Guyana Americas 795 36.77 5.18
## FertilityRate LifeExpectancy ChildMortality CellularSubscribers
## 166 3.48 50 79.7 63.70
## 107 NA 60 37.9 NA
## 160 2.44 58 44.6 126.83
## 88 2.52 67 18.7 155.74
## 73 2.64 63 35.2 69.94
## LiteracyRate GNI PrimarySchoolEnrollmentMale
## 166 87.4 5930 NA
## 107 NA NA NA
## 160 NA 10710 NA
## 88 99.7 11250 NA
## 73 NA NA 82.4
## PrimarySchoolEnrollmentFemale .src .rnorm FertilityRate.nonNA
## 166 NA Train 0.6179858 3.48
## 107 NA Train 0.3104807 2.53
## 160 NA Train 1.6756969 2.44
## 88 NA Train -1.0491770 2.52
## 73 85.9 Train 0.7690422 2.64
## CellularSubscribers.nonNA LiteracyRate.nonNA GNI.nonNA
## 166 63.70 87.4 5930
## 107 104.55 89.5 540
## 160 126.83 84.5 10710
## 88 155.74 99.7 11250
## 73 69.94 82.6 5930
## PrimarySchoolEnrollmentMale.nonNA PrimarySchoolEnrollmentFemale.nonNA
## 166 93.9 94.5
## 107 93.3 94.5
## 160 93.3 94.4
## 88 99.2 99.2
## 73 82.4 85.9
## Region.fctr LifeExpectancy.predict.Final.rf
## 166 Africa 53.72063
## 107 Western Pacific 63.21903
## 160 Africa 61.14077
## 88 Europe 70.03887
## 73 Americas 65.59923
## LifeExpectancy.predict.Final.rf.err .label
## 166 3.720633 Swaziland
## 107 3.219033 Marshall Islands
## 160 3.140767 South Africa
## 88 3.038867 Kazakhstan
## 73 2.599233 Guyana
dsp_feats_vctr <- c(NULL)
for(var in grep(".importance", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
print(glb_trnobs_df[glb_trnobs_df$UniqueID %in% FN_OOB_ids,
grep(glb_rsp_var, names(glb_trnobs_df), value=TRUE)])## [1] LifeExpectancy LifeExpectancy.predict.Final.rf
## [3] LifeExpectancy.predict.Final.rf.err
## <0 rows> (or 0-length row.names)print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## [1] "LifeExpectancy.predict.Final.rf"
## [2] "LifeExpectancy.predict.Final.rf.err"for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## character(0)for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 15 fit.data.training 8 1 71.906 75.466 3.56
## 16 predict.data.new 9 0 75.467 NA NAStep 9.0: predict data new
# Compute final model predictions
glb_newobs_df <- glb_get_predictions(glb_newobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=ifelse(glb_is_classification && glb_is_binomial,
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"], NULL))
## Country Region Population Under15 Over60
## 129 Pakistan Eastern Mediterranean 179000 34.31 6.44
## 98 Libya Eastern Mediterranean 6155 29.45 6.96
## 16 Belarus Europe 9405 15.10 19.31
## 91 Kuwait Eastern Mediterranean 3250 24.90 3.80
## 142 Russian Federation Europe 143000 15.45 18.60
## 52 Dominican Republic Americas 10277 30.53 8.97
## FertilityRate LifeExpectancy ChildMortality CellularSubscribers
## 129 3.35 67 85.9 61.61
## 98 2.47 65 15.4 155.70
## 16 1.47 71 5.2 111.88
## 91 2.65 80 11.0 175.09
## 142 1.51 69 10.3 179.31
## 52 2.55 73 27.1 87.22
## LiteracyRate GNI PrimarySchoolEnrollmentMale
## 129 NA 2870 81.3
## 98 89.2 NA NA
## 16 NA 14460 NA
## 91 NA NA NA
## 142 99.6 20560 NA
## 52 89.5 9420 95.5
## PrimarySchoolEnrollmentFemale .src .rnorm FertilityRate.nonNA
## 129 66.5 Test 0.07455118 3.35
## 98 NA Test 0.68430943 2.47
## 16 NA Test 0.48545998 1.47
## 91 NA Test 0.96252797 2.65
## 142 NA Test 0.73649596 1.51
## 52 90.4 Test 1.08079950 2.55
## CellularSubscribers.nonNA LiteracyRate.nonNA GNI.nonNA
## 129 61.61 56.2 2870
## 98 155.70 89.2 10440
## 16 111.88 93.2 14460
## 91 175.09 96.3 3640
## 142 179.31 99.6 20560
## 52 87.22 89.5 9420
## PrimarySchoolEnrollmentMale.nonNA PrimarySchoolEnrollmentFemale.nonNA
## 129 81.3 66.5
## 98 98.9 99.3
## 16 96.5 96.5
## 91 96.5 97.0
## 142 97.0 97.3
## 52 95.5 90.4
## Region.fctr LifeExpectancy.predict.Final.rf
## 129 Eastern Mediterranean 55.39683
## 98 Eastern Mediterranean 73.97303
## 16 Europe 78.17203
## 91 Eastern Mediterranean 73.98070
## 142 Europe 74.07800
## 52 Americas 67.95067
## LifeExpectancy.predict.Final.rf.err
## 129 11.603167
## 98 8.973033
## 16 7.172033
## 91 6.019300
## 142 5.078000
## 52 5.049333if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_newobs_df, mdl_id =
## glb_fin_mdl_id): Limiting important feature scatter plots to 5 out of 11
## Country Region Population Under15 Over60
## 129 Pakistan Eastern Mediterranean 179000 34.31 6.44
## 98 Libya Eastern Mediterranean 6155 29.45 6.96
## 16 Belarus Europe 9405 15.10 19.31
## 91 Kuwait Eastern Mediterranean 3250 24.90 3.80
## 142 Russian Federation Europe 143000 15.45 18.60
## FertilityRate LifeExpectancy ChildMortality CellularSubscribers
## 129 3.35 67 85.9 61.61
## 98 2.47 65 15.4 155.70
## 16 1.47 71 5.2 111.88
## 91 2.65 80 11.0 175.09
## 142 1.51 69 10.3 179.31
## LiteracyRate GNI PrimarySchoolEnrollmentMale
## 129 NA 2870 81.3
## 98 89.2 NA NA
## 16 NA 14460 NA
## 91 NA NA NA
## 142 99.6 20560 NA
## PrimarySchoolEnrollmentFemale .src .rnorm FertilityRate.nonNA
## 129 66.5 Test 0.07455118 3.35
## 98 NA Test 0.68430943 2.47
## 16 NA Test 0.48545998 1.47
## 91 NA Test 0.96252797 2.65
## 142 NA Test 0.73649596 1.51
## CellularSubscribers.nonNA LiteracyRate.nonNA GNI.nonNA
## 129 61.61 56.2 2870
## 98 155.70 89.2 10440
## 16 111.88 93.2 14460
## 91 175.09 96.3 3640
## 142 179.31 99.6 20560
## PrimarySchoolEnrollmentMale.nonNA PrimarySchoolEnrollmentFemale.nonNA
## 129 81.3 66.5
## 98 98.9 99.3
## 16 96.5 96.5
## 91 96.5 97.0
## 142 97.0 97.3
## Region.fctr LifeExpectancy.predict.Final.rf
## 129 Eastern Mediterranean 55.39683
## 98 Eastern Mediterranean 73.97303
## 16 Europe 78.17203
## 91 Eastern Mediterranean 73.98070
## 142 Europe 74.07800
## LifeExpectancy.predict.Final.rf.err .label
## 129 11.603167 Pakistan
## 98 8.973033 Libya
## 16 7.172033 Belarus
## 91 6.019300 Kuwait
## 142 5.078000 Russian Federation
if (glb_is_classification && glb_is_binomial) {
submit_df <- glb_newobs_df[, c(glb_id_vars,
paste0(glb_rsp_var_out, glb_fin_mdl_id, ".prob"))]
names(submit_df)[2] <- "Probability1"
} else submit_df <- glb_newobs_df[, c(glb_id_vars,
paste0(glb_rsp_var_out, glb_fin_mdl_id))]
write.csv(submit_df,
paste0(gsub(".", "_", paste0(glb_out_pfx, glb_fin_mdl_id), fixed=TRUE),
"_submit.csv"), row.names=FALSE)
# print(orderBy(~ -max.auc.OOB, glb_models_df[, c("model_id",
# "max.auc.OOB", "max.Accuracy.OOB")]))
if (glb_is_classification && glb_is_binomial)
print(glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"])
print(sprintf("glb_sel_mdl_id: %s", glb_sel_mdl_id))## [1] "glb_sel_mdl_id: All.X.no.rnorm.rf"print(sprintf("glb_fin_mdl_id: %s", glb_fin_mdl_id))## [1] "glb_fin_mdl_id: Final.rf"print(dim(glb_fitobs_df))## [1] 139 22print(dsp_models_df)## model_id min.RMSE.OOB
## All.X.no.rnorm.rf All.X.no.rnorm.rf 3.066581
## All.X.lm All.X.lm 3.119837
## All.X.glm All.X.glm 3.119837
## Low.cor.X.lm Low.cor.X.lm 3.215729
## Max.cor.Y.cv.0.cp.0.rpart Max.cor.Y.cv.0.cp.0.rpart 3.251075
## Max.cor.Y.lm Max.cor.Y.lm 3.375750
## Interact.High.cor.Y.lm Interact.High.cor.Y.lm 3.486467
## Max.cor.Y.rpart Max.cor.Y.rpart 4.307076
## All.X.no.rnorm.rpart All.X.no.rnorm.rpart 4.307076
## MFO.lm MFO.lm 8.838414
## Max.cor.Y.cv.0.rpart Max.cor.Y.cv.0.rpart 8.880209
## max.R.sq.OOB max.Adj.R.sq.fit
## All.X.no.rnorm.rf 0.880682838 NA
## All.X.lm 0.876570916 0.914318813
## All.X.glm 0.876570916 NA
## Low.cor.X.lm 0.868866804 0.908319490
## Max.cor.Y.cv.0.cp.0.rpart 0.865968304 NA
## Max.cor.Y.lm 0.855491202 0.899291417
## Interact.High.cor.Y.lm 0.845856653 0.906961104
## Max.cor.Y.rpart 0.764755963 NA
## All.X.no.rnorm.rpart 0.764755963 NA
## MFO.lm 0.009390806 -0.006185647
## Max.cor.Y.cv.0.rpart 0.000000000 NAif (glb_is_classification) {
print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id))
print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[, glb_rsp_var])$table))
tmp_OOBobs_df <- glb_OOBobs_df[, c("myCategory", predct_accurate_var_name)]
names(tmp_OOBobs_df)[2] <- "accurate.OOB"
aOOB_ctgry_df <- mycreate_xtab_df(tmp_OOBobs_df, names(tmp_OOBobs_df))
aOOB_ctgry_df[is.na(aOOB_ctgry_df)] <- 0
aOOB_ctgry_df <- mutate(aOOB_ctgry_df,
.n.OOB = accurate.OOB.FALSE + accurate.OOB.TRUE,
max.accuracy.OOB = accurate.OOB.TRUE / .n.OOB)
intersect(names(glb_ctgry_df), names(aOOB_ctgry_df))
glb_ctgry_df <- merge(glb_ctgry_df, aOOB_ctgry_df, all=TRUE)
print(orderBy(~-accurate.OOB.FALSE, glb_ctgry_df))
}
dsp_myCategory_conf_mtrx <- function(myCategory) {
print(sprintf("%s OOB::myCategory=%s confusion matrix & accuracy: ",
glb_sel_mdl_id, myCategory))
print(t(confusionMatrix(
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, glb_rsp_var])$table))
print(sum(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory,
predct_accurate_var_name]) /
nrow(glb_OOBobs_df[glb_OOBobs_df$myCategory == myCategory, ]))
err_ids <- glb_OOBobs_df[(glb_OOBobs_df$myCategory == myCategory) &
(!glb_OOBobs_df[, predct_accurate_var_name]), glb_id_vars]
OOB_FNerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 1),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FN errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FNerr_df)))
print(OOB_FNerr_df)
OOB_FPerr_df <- glb_OOBobs_df[(glb_OOBobs_df$UniqueID %in% err_ids) &
(glb_OOBobs_df$Popular == 0),
c(
".clusterid",
"Popular", "Headline", "Snippet", "Abstract")]
print(sprintf("%s OOB::myCategory=%s FP errors: %d", glb_sel_mdl_id, myCategory,
nrow(OOB_FPerr_df)))
print(OOB_FPerr_df)
}
#dsp_myCategory_conf_mtrx(myCategory="OpEd#Opinion#")
#dsp_myCategory_conf_mtrx(myCategory="Business#Business Day#Dealbook")
#dsp_myCategory_conf_mtrx(myCategory="##")
if (glb_is_classification) {
print("FN_OOB_ids:")
print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
glb_txt_vars])
print(dsp_vctr <- colSums(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
setdiff(grep("[HSA].", names(glb_OOBobs_df), value=TRUE),
union(myfind_chr_cols_df(glb_OOBobs_df),
grep(".fctr", names(glb_OOBobs_df), fixed=TRUE, value=TRUE)))]))
}
dsp_hdlpfx_results <- function(hdlpfx) {
print(hdlpfx)
print(glb_OOBobs_df[glb_OOBobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
grep(glb_rsp_var, names(glb_newobs_df), value=TRUE)])
print(dsp_vctr <- colSums(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
setdiff(grep("[HSA]\\.", names(glb_newobs_df), value=TRUE),
union(myfind_chr_cols_df(glb_newobs_df),
grep(".fctr", names(glb_newobs_df), fixed=TRUE, value=TRUE)))]))
print(dsp_vctr <- dsp_vctr[dsp_vctr != 0])
print(glb_newobs_df[glb_newobs_df$Headline.pfx %in% c(hdlpfx),
union(names(dsp_vctr), myfind_chr_cols_df(glb_newobs_df))])
}
#dsp_hdlpfx_results(hdlpfx="Ask Well::")
# print("myMisc::|OpEd|blank|blank|1:")
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% c(6446),
# grep(glb_rsp_var, names(glb_OOBobs_df), value=TRUE)])
# print(glb_OOBobs_df[glb_OOBobs_df$UniqueID %in% FN_OOB_ids,
# c("WordCount", "WordCount.log", "myMultimedia",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"), ],
# c(glb_rsp_var, "myMultimedia")))
# dsp_chisq.test(Headline.contains="[Vi]deo")
# print(glb_allobs_df[sel_obs(Headline.contains="[Vv]ideo"),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline")])
# print(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola", Popular=1),
# c(glb_rsp_var, "Popular", "myMultimedia", "Headline",
# "NewsDesk", "SectionName", "SubsectionName")])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.ConditionalX.y & is.na(importance))[,
# c("is.ConditionalX.y",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, !is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
# print(subset(glb_feats_df, is.na(importance))[,
# c("zeroVar", "nzv", "myNearZV",
# grep("importance", names(glb_feats_df), fixed=TRUE, value=TRUE))])
print(orderBy(as.formula(paste0("~ -", glb_sel_mdl_id, ".importance")), glb_featsimp_df))## All.X.no.rnorm.rf.importance
## ChildMortality 100.00000000
## GNI.nonNA 2.43788756
## LiteracyRate.nonNA 0.92477967
## FertilityRate.nonNA 0.71952386
## Over60 0.69741081
## Population 0.61068053
## PrimarySchoolEnrollmentMale.nonNA 0.53976660
## Under15 0.52805249
## Region.fctrAfrica 0.52203704
## PrimarySchoolEnrollmentFemale.nonNA 0.51930063
## CellularSubscribers.nonNA 0.46309748
## Region.fctrAmericas 0.12263012
## Region.fctrSouth-East Asia 0.03026750
## Region.fctrWestern Pacific 0.02854844
## Region.fctrEurope 0.02524768
## importance Final.rf.importance
## ChildMortality 100.00000000 100.00000000
## GNI.nonNA 2.43788756 2.43788756
## LiteracyRate.nonNA 0.92477967 0.92477967
## FertilityRate.nonNA 0.71952386 0.71952386
## Over60 0.69741081 0.69741081
## Population 0.61068053 0.61068053
## PrimarySchoolEnrollmentMale.nonNA 0.53976660 0.53976660
## Under15 0.52805249 0.52805249
## Region.fctrAfrica 0.52203704 0.52203704
## PrimarySchoolEnrollmentFemale.nonNA 0.51930063 0.51930063
## CellularSubscribers.nonNA 0.46309748 0.46309748
## Region.fctrAmericas 0.12263012 0.12263012
## Region.fctrSouth-East Asia 0.03026750 0.03026750
## Region.fctrWestern Pacific 0.02854844 0.02854844
## Region.fctrEurope 0.02524768 0.02524768print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## character(0)for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## character(0)for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "prdnew_dsk.RData"))
rm(submit_df, tmp_OOBobs_df)## Warning in rm(submit_df, tmp_OOBobs_df): object 'tmp_OOBobs_df' not found# tmp_replay_lst <- replay.petrisim(pn=glb_analytics_pn,
# replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
# "data.new.prediction")), flip_coord=TRUE)
# print(ggplot.petrinet(tmp_replay_lst[["pn"]]) + coord_flip())
glb_chunks_df <- myadd_chunk(glb_chunks_df, "display.session.info", major.inc=TRUE)## label step_major step_minor bgn end elapsed
## 16 predict.data.new 9 0 75.467 79.053 3.586
## 17 display.session.info 10 0 79.053 NA NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor bgn end elapsed
## 2 inspect.data 2 0 8.260 22.189 13.929
## 10 fit.models 7 0 28.564 42.492 13.928
## 11 fit.models 7 1 42.492 55.374 12.883
## 12 fit.models 7 2 55.375 64.353 8.978
## 14 fit.data.training 8 0 67.651 71.905 4.255
## 16 predict.data.new 9 0 75.467 79.053 3.586
## 15 fit.data.training 8 1 71.906 75.466 3.560
## 13 fit.models 7 3 64.353 67.650 3.298
## 3 cleanse.data 2 1 22.189 25.021 2.832
## 4 manage.missing.data 2 2 25.022 26.252 1.230
## 6 extract.features 3 0 26.277 27.379 1.102
## 8 select.features 5 0 27.658 28.272 0.614
## 1 import.data 1 0 7.716 8.259 0.544
## 9 partition.data.training 6 0 28.272 28.564 0.292
## 7 cluster.data 4 0 27.379 27.658 0.279
## 5 encode.data 2 3 26.252 26.277 0.025
## duration
## 2 13.929
## 10 13.928
## 11 12.882
## 12 8.978
## 14 4.254
## 16 3.586
## 15 3.560
## 13 3.297
## 3 2.832
## 4 1.230
## 6 1.102
## 8 0.614
## 1 0.543
## 9 0.292
## 7 0.279
## 5 0.025
## [1] "Total Elapsed Time: 79.053 secs"
## label step_major step_minor bgn end elapsed duration
## 5 fit.models_1_rf 5 0 51.687 55.369 3.682 3.682
## 4 fit.models_1_rpart 4 0 48.936 51.687 2.751 2.751
## 3 fit.models_1_glm 3 0 46.642 48.936 2.294 2.294
## 2 fit.models_1_lm 2 0 44.407 46.642 2.235 2.235
## 1 fit.models_1_bgn 1 0 44.393 44.407 0.014 0.014
## [1] "Total Elapsed Time: 55.369 secs"
## R version 3.2.0 (2015-04-16)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.3 (Yosemite)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] grid parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] gdata_2.16.1 randomForest_4.6-10 rpart.plot_1.5.2
## [4] rpart_4.1-9 reshape2_1.4.1 mice_2.22
## [7] Rcpp_0.11.6 plyr_1.8.2 caTools_1.17.1
## [10] doMC_1.3.3 iterators_1.0.7 foreach_1.4.2
## [13] doBy_4.5-13 survival_2.38-1 caret_6.0-47
## [16] ggplot2_1.0.1 lattice_0.20-31
##
## loaded via a namespace (and not attached):
## [1] compiler_3.2.0 RColorBrewer_1.1-2 formatR_1.2
## [4] nloptr_1.0.4 bitops_1.0-6 tools_3.2.0
## [7] digest_0.6.8 lme4_1.1-7 evaluate_0.7
## [10] nlme_3.1-120 gtable_0.1.2 mgcv_1.8-6
## [13] Matrix_1.2-1 yaml_2.1.13 brglm_0.5-9
## [16] SparseM_1.6 proto_0.3-10 BradleyTerry2_1.0-6
## [19] stringr_1.0.0 knitr_1.10.5 gtools_3.5.0
## [22] nnet_7.3-9 rmarkdown_0.6.1 minqa_1.2.4
## [25] car_2.0-25 magrittr_1.5 scales_0.2.4
## [28] codetools_0.2-11 htmltools_0.2.6 MASS_7.3-40
## [31] splines_3.2.0 pbkrtest_0.4-2 colorspace_1.2-6
## [34] labeling_0.3 quantreg_5.11 stringi_0.4-1
## [37] munsell_0.4.2